reliable, energy-aware cross-layer protocol …4232/datastream...reliable, energy-aware cross-layer...

RELIABLE, ENERGY-AWARE CROSS-LAYER PROTOCOL FOR WIRELESS

SENSOR NETWORKS

by

Ahmed Badi

A Dissertation Submitted to the Faculty of

The College of Engineering and Computer Science

in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

Florida Atlantic University

Boca Raton, FL

December 2009

c© Copyright by Ahmed Badi 2009

ii

ACKNOWLEDGMENTS

This is an opportunity to express my gratitude to Professor Imad Mahgoub,

for his guidance and continuous support. The success of this work is owing to the

open discussions that Dr.Mahgoub and I have had on various issues of the subject

matter. I am grateful for his constructive comments on my work. I also would like

to thank my dissertation committee members, Dr. Mihaela Cardei, Dr. Ed Callaway,

Dr. Mohammad Ilyas and Dr. A.Kader Mazouz. I benefited enormously from their

feedback, analyses and comments. Their input was vital in shaping this work. In

my various interactions with the committee members, I learned to be precise in my

discussions and rigorous in my analysis.

This work would not have been possible without the help of many people to

whom I would like to pay special thanks. First, I would like to thank my family

for their understanding, support and love. It is their countless sacrifices that made

this work possible. Special gratitude goes to my friends, the Ng-A-Fook family who

always been on my side through my studies. I will always be in debt to them for their

help, support and friendship. Grateful thanks are given to Dr. Martin Solomon, Dr.

Borko Furht, Dr. Clovis Tondo, Dr. Lofton Bullard, Dr. Ali Zilouchian, Dr. Thomas

Fernandez, Dr. Abhijit Pandya, and all my friends for their kind support.

I would like to thank the faculty and staff of the Department of Computer

Science and Engineering at Florida Atlantic University for their assistance and support,

and the staff of the Technical Services Group at the College of Engineering for their

help, efficiency and high professional standards. The Department of Defense through

Pragmatics Inc. has provided most of the funds to support this research work. I am

grateful for this support.

iv

ABSTRACT

Author: Ahmed Badi

Title: RELIABLE, ENERGY-AWARE CROSS-LAYERPROTOCOL FOR WIRELESS SENSOR NETWORKS

Institution: Florida Atlantic University

Dissertation Advisor: Dr. Imad Mahgoub

Degree: Doctor of Philosophy

Year: 2009

This research addresses communication reliability in the highly constrained wire-

less sensor networks environment. We propose a cross-layer, reliable wireless sensor

protocol design. The protocol benefits from the body of research in the two areas of

wireless sensors reliability research and wireless sensors energy conservation research.

The protocol introduces a new energy saving technique that considers reliability as a de-

sign parameter and constraint. The protocol also introduces a new back-off algorithm

that dynamically adjusts to the data messages reliability needs. Other cross-layer

techniques that the protocol introduces are dynamic MAC retry limit and dynamic

transmission power setting that is also based on the messages reliability requirements.

Cross layer design is defined as the interaction between the different stack layers

with the goal of improving performance. It has been used in ad hoc wireless systems to

improve throughput, latency, and quality of service (QoS). The improvements gained

v

in performance come at a price. This includes decreased architecture modularity and

designs may be hard to debug, maintain or upgrade.

Cross-layer design is valuable for wireless sensor networks due to the severe

resource constraints. The proposed protocol uses cross-layer design as a performance

and energy optimization technique. Nevertheless, the protocol avoids introducing layer

interdependencies by preserving the stack architecture and optimizes the overall sys-

tem energy and reliability performance by information sharing. The information is

embedded as flags in the data and control messages that are moving through the stack.

Each layer reads these flags and adjusts its performance and handling of the message

accordingly.

The performance of the proposed protocol is evaluated using simulation model-

ing. The reference protocol used for evaluation is APTEEN. We developed simulation

programs for the proposed protocol and for APTEEN protocol using the JiST/SWANS

simulation tool.

The performance evaluation results show that the proposed protocol achieves better

energy performance than the reference protocol. Several scalability experiments show

that the proposed protocol scales well and has better performance for large networks.

Also, exhaustive bandwidth utilization experiments show that for heavily-utilized or

congested networks, the proposed protocol has high reliability in delivering messages

classified as important.

vi

To my parents, Kamal Badi and Sayda Satti

To Sahar, Omer, Yasmeen and Mazen

To Ibrahim, Mahasin, Nawal and Sukaina

This work is especially dedicated to Professor Mustafa Badi

˜Ahmed Kamal Badi

RELIABLE, ENERGY-AWARE CROSS-LAYER PROTOCOL FOR WIRELESS

SENSOR NETWORKS

FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xivTABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xx

1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1 Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Characteristics of Wireless Sensor Networks . . . . . . . . . . . . . . . 2

1.2.1 Low energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.2 Self-Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2.3 Scalability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2.4 In-network signal processing . . . . . . . . . . . . . . . . . . . . 31.2.5 In-network query processing . . . . . . . . . . . . . . . . . . . . 4

1.3 Wireless Sensors Challenges . . . . . . . . . . . . . . . . . . . . . . . . 41.4 Wireless Sensor Networks Reliability Research . . . . . . . . . . . . . . 51.5 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.6 Dissertation Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 71.7 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2 CLASSIFICATION OF WIRELESS SENSOR NETWORKSENERGY OPTIMIZATION PROTOCOLS . . . . . . . . . . . . . . . 12

2.1 The Open Systems Interconnection (OSI) Network Stack . . . . . . . . 12

2.1.1 Application Layer . . . . . . . . . . . . . . . . . . . . . . . . . . 132.1.2 Transport Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.1.3 Network Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.1.4 Medium Access Control (MAC) Layer . . . . . . . . . . . . . . 15

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2.1.5 Physical Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.2 OSI Layers Optimization for Wireless Sensors . . . . . . . . . . . . . . 17

2.2.1 Application Layer . . . . . . . . . . . . . . . . . . . . . . . . . . 182.2.2 Transport Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.2.3 Network/ Routing Layer . . . . . . . . . . . . . . . . . . . . . . 19

2.2.3.1 Low-Energy Adaptive Clustering Hierarchy (LEACH) . 192.2.3.2 Power-Efficient Gathering in Sensor Information

Systems (PEGASIS) . . . . . . . . . . . . . . . . . . . 202.2.3.3 Threshold sensitive Energy efficient Sensor Network

Protocol/ Adaptive Periodic Threshold-sensitiveEnergy Efficient Sensor Network Protocol(TEEN/APTEEN) . . . . . . . . . . . . . . . . . . . . 22

2.2.3.4 Directed Diffusion . . . . . . . . . . . . . . . . . . . . 242.2.3.5 Geographical and Energy Aware Routing Protocol

(GEAR) . . . . . . . . . . . . . . . . . . . . . . . . . . 242.2.3.6 Sensor Protocols for Information via Negotiation

(SPIN) . . . . . . . . . . . . . . . . . . . . . . . . . . 252.2.3.7 Cost-effective Maximum Lifetime Routing (CMLR) . . 25

2.2.4 MAC Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.2.4.1 Sensor MAC (SMAC) . . . . . . . . . . . . . . . . . . 262.2.4.2 Delay MAC (DMAC) . . . . . . . . . . . . . . . . . . 272.2.4.3 TMAC . . . . . . . . . . . . . . . . . . . . . . . . . . . 272.2.4.4 WiseMAC . . . . . . . . . . . . . . . . . . . . . . . . . 282.2.4.5 Group-based Medium Access Control (GMAC) . . . . 292.2.4.6 Traffic-Adaptive Medium Access Protocol (TRAMA) . 31

2.2.5 Physical and Radio Layer . . . . . . . . . . . . . . . . . . . . . 312.2.6 Summary of OSI Layers Optimization Techniques for Wireless

Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.3 Cross-layer Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.3.1 The Need for Cross-layer Optimizations in Wireless SensorNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.3.2 WSNs Cross-layer Energy Balance and Energy PerformanceAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.3.2.1 Cross-layer Energy Balance . . . . . . . . . . . . . . . 35

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2.3.2.2 Cross-layer Energy Performance Analysis . . . . . . . . 36

2.3.3 Multi-layer Cross-layer Optimization Techniques . . . . . . . . . 37

2.3.3.1 Cross-layer Optimization Using Forward ErrorCorrection . . . . . . . . . . . . . . . . . . . . . . . . . 38

2.3.3.2 Cross-layer Optimization Using Feedback OptimizationAgents . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

2.3.4 WSNs Cross-layer Design Challenges and Open Research Issues 402.3.5 Cross-layer Design Challenges . . . . . . . . . . . . . . . . . . . 402.3.6 Cross-layer Open Research Issues . . . . . . . . . . . . . . . . . 41

2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3 RELIABILITY IN WIRELESS SENSOR NETWORKS . . . . . . . 45

3.1 Importance of Wireless Sensor Networks (WSNs) Reliability . . . . . . 453.2 Classification of WSNs Reliable Protocols . . . . . . . . . . . . . . . . . 47

3.2.1 Transport Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

3.2.1.1 Event-To-Sink Transport (ESRT) Protocol . . . . . . . 483.2.1.2 Reliable Multi-Segment Transport Protocol . . . . . . 503.2.1.3 Analysis and Classification of WSNs Reliable Transport

Protocols . . . . . . . . . . . . . . . . . . . . . . . . . 523.2.1.4 Improving Transport Reliability by Using MAC Layer

ARQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.2.2 Network Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

3.2.2.1 Reliable Routing Using Graph Theory Analysis . . . . 563.2.2.2 Multiple Routes and Erasure Codes Reliable Protocols 573.2.2.3 Reliable Routing Using Link Connectivity Statistics . . 60

3.2.3 MAC Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

3.2.3.1 Reliable Protocols Using MAC Layer Retransmission . 613.2.3.2 Reliable Protocols Using MAC Layer Contention

Window Size . . . . . . . . . . . . . . . . . . . . . . . 623.2.3.3 Reliable Protocols Using MAC RTS/CTS Messages . . 633.2.3.4 Reliable Protocols Using MAC ACK Messages . . . . . 64

3.2.4 Radio and Physical Layer . . . . . . . . . . . . . . . . . . . . . 64

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3.2.5 Cross-Layer Reliability . . . . . . . . . . . . . . . . . . . . . . . 67

3.2.5.1 Cross-Layer Reliable Protocol Using EmbeddedMessage Reliability Flag . . . . . . . . . . . . . . . . . 68

3.3 Wireless Sensor Networks Reliability Techniques . . . . . . . . . . . . . 693.4 Wireless Sensor Networks Reliability Challenges and Open Issues . . . 69

3.4.1 Wireless Sensor Networks Reliability Challenges . . . . . . . . . 693.4.2 Wireless Sensor Networks Reliability Open Issues . . . . . . . . 70

3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

4 RELIABLE, ENERGY-AWARE CROSS-LAYER PROTOCOLFOR WIRELESS SENSOR NETWORKS . . . . . . . . . . . . . . . . 72

4.1 Classifying Wireless Sensor Networks Research . . . . . . . . . . . . . . 72

4.1.1 Wireless Sensor Networks Energy Optimization Research . . . . 734.1.2 Wireless Sensor Networks Reliability Research . . . . . . . . . . 734.1.3 Bridging the Gap . . . . . . . . . . . . . . . . . . . . . . . . . . 74

4.2 Proposed Protocol Network Settings . . . . . . . . . . . . . . . . . . . 744.3 Proposed Protocol Architecture . . . . . . . . . . . . . . . . . . . . . . 75

4.3.1 Protocol Messages . . . . . . . . . . . . . . . . . . . . . . . . . 75

4.3.1.1 Proposed Protocol Messages’ Reliability Settings . . . 774.3.1.2 Periodic Report Messages . . . . . . . . . . . . . . . . 774.3.1.3 Event Reporting Messages . . . . . . . . . . . . . . . . 784.3.1.4 Infrastructure Communication Messages . . . . . . . . 794.3.1.5 Proposed Hello and Hello-reply Messages . . . . . . . . 80

4.3.2 Proposed Protocol Routing and Clustering Algorithms . . . . . 81

4.3.2.1 Wireless Communication Challenges and ExistingSolutions . . . . . . . . . . . . . . . . . . . . . . . . . 81

4.3.2.2 Proposed Individualized Reliable Link Power SettingsUsing the Hello Exchange . . . . . . . . . . . . . . . . 82

4.3.2.3 Proposed Link Rating parameter . . . . . . . . . . . . 854.3.2.4 Clusters Formation Using the Proposed Link Rating

Parameter . . . . . . . . . . . . . . . . . . . . . . . . . 86

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4.3.2.5 Proposed Protocol Energy Optimization UnderReliability Constraints . . . . . . . . . . . . . . . . . . 87

4.3.3 Proposed MAC Dynamic Back-off Algorithm . . . . . . . . . . . 874.3.4 Proposed Protocol Startup Phase . . . . . . . . . . . . . . . . . 894.3.5 Protocol Steady-State Operation . . . . . . . . . . . . . . . . . 90

4.4 Summary of Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 914.5 Summary of Proposed Protocol’s Cross-layer Techniques . . . . . . . . 924.6 Related Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

4.6.1 LEACH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 954.6.2 TEEN/APTEEN . . . . . . . . . . . . . . . . . . . . . . . . . . 954.6.3 ESRT Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . 964.6.4 ETX, Erasures Codes Protocols . . . . . . . . . . . . . . . . . . 974.6.5 Cross-layer Protocols . . . . . . . . . . . . . . . . . . . . . . . . 98

4.6.5.1 Benefits of Cross-layer Designs . . . . . . . . . . . . . 984.6.5.2 Drawbacks of Cross-layer Designs . . . . . . . . . . . . 994.6.5.3 Avoiding Cross-layer Drawbacks in Proposed Protocol 99

4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

5 RELIABLE, ENERGY-AWARE PROTOCOL PERFORMANCEEVALUATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

5.1 Proposed Protocol Performance Evaluation Method . . . . . . . . . . . 1025.2 Performance Evaluation Tools . . . . . . . . . . . . . . . . . . . . . . . 103

5.2.1 Tool Selection Criteria . . . . . . . . . . . . . . . . . . . . . . . 1035.2.2 Simulation Tools Survey . . . . . . . . . . . . . . . . . . . . . . 103

5.2.2.1 Opnet . . . . . . . . . . . . . . . . . . . . . . . . . . . 1045.2.2.2 GloMoSim . . . . . . . . . . . . . . . . . . . . . . . . . 1045.2.2.3 NS-2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1055.2.2.4 PDNS . . . . . . . . . . . . . . . . . . . . . . . . . . . 1055.2.2.5 JiST/SWANS . . . . . . . . . . . . . . . . . . . . . . . 105

5.2.3 JiST/SWANS Wireless Ad-hoc Network Simulator . . . . . . . . 106

5.2.3.1 Upgrading JiST/SWANS to a Wireless SensorNetworks Simulator . . . . . . . . . . . . . . . . . . . 108

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5.2.3.2 Validating the JiST/SWANS for WSNs Simulation . . 109

5.3 Proposed Protocol Evaluation Scenarios . . . . . . . . . . . . . . . . . 110

5.3.1 Routing and Cluster Head Selection Evaluation Scenarios . . . . 1115.3.2 MAC layer Evaluation Scenarios . . . . . . . . . . . . . . . . . . 1115.3.3 Radio and Physical Layers Evaluation Scenarios . . . . . . . . . 1135.3.4 Optimizing the Hello Messages Exchange . . . . . . . . . . . . . 114

5.4 Radio Model for Simulation . . . . . . . . . . . . . . . . . . . . . . . . 116

5.4.1 Disk Radio Model . . . . . . . . . . . . . . . . . . . . . . . . . . 1175.4.2 Rayleigh fading Radio Model . . . . . . . . . . . . . . . . . . . 1185.4.3 Rician fading Radio Model . . . . . . . . . . . . . . . . . . . . . 1185.4.4 First Order Radio Model . . . . . . . . . . . . . . . . . . . . . . 1185.4.5 Radio Model Used for Performance Analysis . . . . . . . . . . . 119

5.5 Performance Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

5.5.1 Proposed Protocol performance using Default Parameters . . . . 122

5.5.1.1 Energy Performance . . . . . . . . . . . . . . . . . . . 1235.5.1.2 Reliability Performance . . . . . . . . . . . . . . . . . 1235.5.1.3 Latency Performance . . . . . . . . . . . . . . . . . . . 124

5.5.2 Varying Number of Reports per Round . . . . . . . . . . . . . . 126

5.5.2.1 Energy Performance . . . . . . . . . . . . . . . . . . . 1265.5.2.2 Reliability Performance . . . . . . . . . . . . . . . . . 127

5.5.3 Varying Maximum Number of Hello Messages . . . . . . . . . . 127


5.5.4 Varying Packet Size . . . . . . . . . . . . . . . . . . . . . . . . . 130


5.5.5 Varying Transmit-Receive Energy Ratio . . . . . . . . . . . . . 134

5.5.5.1 Energy Performance . . . . . . . . . . . . . . . . . . . 135

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5.5.5.2 Reliability Performance . . . . . . . . . . . . . . . . . 136

5.5.6 Varying Network size . . . . . . . . . . . . . . . . . . . . . . . . 137

5.5.6.1 Energy Performance . . . . . . . . . . . . . . . . . . . 1385.5.6.2 Reliability Performance . . . . . . . . . . . . . . . . . 1395.5.6.3 Latency Performance . . . . . . . . . . . . . . . . . . . 140

5.5.7 Varying Messages Inter-arrival Time . . . . . . . . . . . . . . . 141

5.5.7.1 Energy Performance . . . . . . . . . . . . . . . . . . . 1425.5.7.2 Reliability Performance . . . . . . . . . . . . . . . . . 1435.5.7.3 Latency Performance . . . . . . . . . . . . . . . . . . . 1455.5.7.4 Impact of Different Retry Limits . . . . . . . . . . . . 146

5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

6 CONCLUSIONS AND FUTURE WORK . . . . . . . . . . . . . . . . 155

6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1556.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162

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FIGURES

1.1 Components of sensor device. . . . . . . . . . . . . . . . . . . . . . . . . 3

2.1 OSI five layer protocol stack model. . . . . . . . . . . . . . . . . . . . . . 14

2.2 LEACH protocol configuration. . . . . . . . . . . . . . . . . . . . . . . . 21

2.3 PEGASIS protocol configuration. . . . . . . . . . . . . . . . . . . . . . . 22

2.4 S-MAC periodic listen and sleep schedule. . . . . . . . . . . . . . . . . . 27

2.5 DMAC staggered listen and sleep schedule. . . . . . . . . . . . . . . . . . 28

2.6 Cross-layer optimization framework [87]. . . . . . . . . . . . . . . . . . . 41

2.7 Adaptive cross-layer design operation [18]. . . . . . . . . . . . . . . . . . 42

3.1 Classification of the related work in WSNs [20]. . . . . . . . . . . . . . . 46

3.2 ESRT Event Radius [75]. . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.3 EESRT Five reporting regions [75]. . . . . . . . . . . . . . . . . . . . . . 50

3.4 Reliability block diagram for RMST [78]. . . . . . . . . . . . . . . . . . . 53

3.5 Reliability block diagram for ESRT [78]. . . . . . . . . . . . . . . . . . . 53

3.6 Reliability block diagram for RBC [78]. . . . . . . . . . . . . . . . . . . . 54

3.7 Probability of arrival across 40 hops with an average error rate of 0.10 per

hop, given R retries per hop [86]. . . . . . . . . . . . . . . . . . . . . . . 55

3.8 Probability of arrival across 6 hops [86] . . . . . . . . . . . . . . . . . . . 56

3.9 (a) A wireless network, (b) The Graph model.[3]. . . . . . . . . . . . . . 57

xiv

3.10 Network Graph [3]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

3.11 Erasure Code Mechanism [44]. . . . . . . . . . . . . . . . . . . . . . . . . 59

3.12 RTS/CTS handshake to protect longer packets bursts [94]. . . . . . . . . 64

3.13 Reception probability of all links in a network with a line topology [101]. 66

3.14 Reception probability variation over time across a single link [101]. . . . . 67

4.1 Bridging the gap, reliable, energy-aware protocol design. . . . . . . . . . 75

4.2 Proposed protocol network setting. . . . . . . . . . . . . . . . . . . . . . 76

4.3 Application and infrastructure communication message format. . . . . . . 77

4.4 Hello message format. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

4.5 Hello-reply message format. . . . . . . . . . . . . . . . . . . . . . . . . . 81

4.6 Hello messages exchange flowchart. . . . . . . . . . . . . . . . . . . . . . 83

4.7 Minimum number of Hello messages exchanged. . . . . . . . . . . . . . . 86

4.8 Example of neighborhood table with data. . . . . . . . . . . . . . . . . . 88

4.9 Proposed Reliable Protocol steady-state operation flowchart. . . . . . . . 93

4.10 Proposed protocol Cross-layer techniques. . . . . . . . . . . . . . . . . . . 94

5.1 SWANS system architecture with energy model added. Reproduced from

[10]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

5.2 JiST/WANS additional components for WSNs simulation. . . . . . . . . 109

5.3 Optimized Hello messages exchange. . . . . . . . . . . . . . . . . . . . . . 116

5.4 Hello messages optimization, Variable minimum link power setting. . . . 116

5.5 Optimized Hello messages exchange. . . . . . . . . . . . . . . . . . . . . . 117

5.6 Simulation analysis radio model. . . . . . . . . . . . . . . . . . . . . . . . 119

5.7 Reliable protocol energy performance. . . . . . . . . . . . . . . . . . . . . 124

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5.8 Report messages reliability. . . . . . . . . . . . . . . . . . . . . . . . . . . 124

5.9 Control messages reliability. . . . . . . . . . . . . . . . . . . . . . . . . . 125

5.10 Event messages reliability. . . . . . . . . . . . . . . . . . . . . . . . . . . 125

5.11 Report messages latency. . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

5.12 Control messages latency. . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

5.13 Event messages latency. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

5.14 Varying number of reports per round energy performance. . . . . . . . . . 128

5.15 Report messages reliability. . . . . . . . . . . . . . . . . . . . . . . . . . . 128

5.16 Control messages reliability. . . . . . . . . . . . . . . . . . . . . . . . . . 129

5.17 Event messages reliability. . . . . . . . . . . . . . . . . . . . . . . . . . . 129

5.18 Impact of maximum number of Hello messages on energy performance. . 130

5.19 Report Messages Reliability. . . . . . . . . . . . . . . . . . . . . . . . . . 130

5.20 Control Messages Reliability. . . . . . . . . . . . . . . . . . . . . . . . . . 131

5.21 Event Messages Reliability. . . . . . . . . . . . . . . . . . . . . . . . . . . 131

5.22 Varying packet size energy performance, normal packet size. . . . . . . . 132

5.23 Varying packet size energy performance, large packet size. . . . . . . . . . 132

5.24 Report messages reliability using normal packet sizes. . . . . . . . . . . . 133

5.25 Report messages reliability using large packet sizes. . . . . . . . . . . . . 133

5.26 Control messages reliability using normal packet sizes. . . . . . . . . . . . 133

5.27 Control messages reliability using large packet sizes. . . . . . . . . . . . . 134

5.28 Event messages reliability using normal packet sizes. . . . . . . . . . . . . 134

5.29 Event messages reliability using large packet sizes. . . . . . . . . . . . . . 134

5.30 Energy performance using Tx:Rx ratio = 1:2. . . . . . . . . . . . . . . . 135

xvi

5.31 Energy performance using Tx:Rx ratio = 2:1. . . . . . . . . . . . . . . . 136

5.32 Energy performance using Tx:Rx ratio = 1:2 vs. 1:1 vs. 2:1. . . . . . . . 136

5.33 Report messages reliability, Tx:Rx ratio = 1:2. . . . . . . . . . . . . . . . 137

5.34 Report messages reliability for Tx:Rx ratio = 2:1. . . . . . . . . . . . . . 137

5.35 Control messages reliability for Tx:Rx ratio = 1:2. . . . . . . . . . . . . . 138

5.36 Control messages reliability for Tx:Rx ratio = 2:1. . . . . . . . . . . . . . 138

5.37 Event messages reliability for Tx:Rx ratio = 1:2. . . . . . . . . . . . . . . 138

5.38 Event messages reliability for Tx:Rx ratio = 2:1. . . . . . . . . . . . . . . 139

5.39 Energy performance for large network (1600 nodes). . . . . . . . . . . . . 139

5.40 Report messages reliability performance. . . . . . . . . . . . . . . . . . . 140

5.41 Control messages reliability performance. . . . . . . . . . . . . . . . . . . 140

5.42 Event messages reliability performance. . . . . . . . . . . . . . . . . . . . 140

5.43 Report messages latency performance. . . . . . . . . . . . . . . . . . . . . 141

5.44 Control messages latency performance. . . . . . . . . . . . . . . . . . . . 141

5.45 Event messages latency performance. . . . . . . . . . . . . . . . . . . . . 142

5.46 Energy performance for different messages inter-arrival time, MAC default

retry limit= 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143


retry limit= 4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143


retry limit= 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

5.49 Report messages reliability performance, APTEEN MAC default retry limit=

3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

xvii


4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145


6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

5.52 Control messages reliability performance, APTEEN MAC default retry limit=

3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146


4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146


6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

5.55 Event messages reliability performance, APTEEN MAC default retry limit

= 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147


= 4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148


= 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

5.58 Report messages latency performance, APTEEN MAC default retry limit =

3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148


4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149


6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

xviii

5.61 Control messages latency performance, APTEEN MAC default retry limit

= 3. 150


= 4. 150


= 6. 151

5.64 Event messages latency performance, APTEEN MAC default retry limit =

3. 151


4. 152


6. 152

5.67 Low reliability vs. high reliability messages performance, messages retry

limit= 5. 153

5.68 Low reliability vs. high reliability messages performance, messages retry

limit= 10. 153

5.69 Low reliability vs. high reliability messages performance, low messages retry

limits=5and10. 154

5.70 Maximum number of message retries reached for both high and low impor-

tance messages. 154

XIX

Chapter 1

INTRODUCTION

Wireless sensors are one of the fastest developing new technologies [13], [88], [77].

The availability of small, cheap low power embedded processors, radio transceivers and

sensors, often integrated on a single chip is leading to the use of sensing, computing and

wireless communication for monitoring and interacting with the physical world. These

wireless sensor devices are assembled of the hardware components mentioned above, an

energy source, in most cases battery together with networking and application firmware

and software. Depending on the size of the network and the complexity required of each

sensor, the cost of sensor devices could vary from hundreds of dollars to few dollars.

The size of a single sensor node can also vary. Sensors can be deployed in large numbers

to form networks that are used to collect data or to pervasively monitor the physical

environment.

1.1 Wireless Sensor Networks

A wireless sensor network (WSN) is a telecommunication network consisting

of spatially distributed sensors and a base station. These sensors monitor physical

or environmental conditions in a cooperative manner. Sensors collect data from their

surrounding environment, and use their networking infrastructure to aggregate and

1

send the collected data to the base station. These networks can be more accurately

described as distributed systems where participants agree to receive and forward data

messages sent by other network participants. The sensors self-organize to form dis-

tributed systems that can be used for a variety of purposes. Military applications such

as monitoring of troop movement and target tracking originally motivated the devel-

opment of wireless sensor networks. However, currently, wireless sensor networks are

found in many civilian applications as well.

1.2 Characteristics of Wireless Sensor Networks

The wireless sensors sensing capabilities cover physical measurements of quan-

tities such as temperature, sound, vibration, pressure, moisture, light intensity, mag-

netism, motion, radiation, or pollutants among many other physical and environmental

quantities. Sensors price, size and self-organization features make them cost-effective

solutions for many problems. They can be useful in applications such as security and

surveillance, smart spaces, monitoring of natural habitats and eco-systems, medical

monitoring, battlefield surveillance, health care applications, home automation, traffic

control, industrial process control, and structural health monitoring. Wireless ad-hoc

sensor network design requirements include the following:

1.2.1 Low energy

In many applications the sensors are battery powered as shown in Figure 1.1.

They are usually placed in remote areas where manual service of sensor nodes may not

be possible. In this case, the nodes lifetime will be dependent on the battery’s lifetime,

thereby requiring the optimization of energy consumption.

2

Figure 1.1: Components of sensor device.

1.2.2 Self-Configuration

With the large number of nodes in the network and their potential placement

in hostile locations, individual node configuration is not possible. Therefore, it is

essential that the network be self-configuring. In addition, nodes may fail due to

energy exhaustion, malfunction, or destruction and new nodes may be added to the

network. For these reasons, the network must be able to periodically reconfigure itself

so that it can continue to function. Also, depending on the nature of the application

the network needs to maintain some degree of connectivity.

1.2.3 Scalability

Wireless sensor networks are assumed to have large number of mostly station-

ary sensors. Networks of 10,000 or even 100,000 nodes are envisioned and network

scalability is a major issue.

1.2.4 In-network signal processing

To improve the quality of data collected, it is often useful to fuse data from

multiple sources. This requires the transmission of data and control messages to some

3

master node before sending it to the base station. This will impose some requirements

on the networks architecture.

1.2.5 In-network query processing

The sensor network may collect a large amount of data. This may overwhelm

the user who may not be able to process all this information. Instead, selected nodes

within the network will collect the data from their neighbors and create a representative

message.

1.3 Wireless Sensors Challenges

Wireless sensor networks (WSNs) represent a new networking model. They are

faster and cheaper to deploy than wired networks and other forms of wireless networks.

They can be deployed in inaccessible and hostile environment. Wireless sensor technol-

ogy still has many limitations that need to be addressed. Cost and size constraints re-

sult in severe limitations on energy, memory, processor speed and bandwidth resources.

The limited energy places the constraint that algorithms suggested for wireless sensor

networks must be energy efficient. The low processing speed constraint necessitates

that wireless sensor network algorithms cannot afford to be computationally intensive.

The limited memory constraints place restrictions on the buffering demands for wireless

sensor networks algorithms. The sheer number of sensors in a single network means

that wireless sensor networking algorithms must be scalable. Another challenge stems

for the fact that as nodes run out of energy, fresh ones are added to the network to

replace them. This constantly changing network places demands that mechanisms de-

signed for wireless sensor networks must be able to function correctly in this dynamic

4

environment. Probably the most important constraint is the limited energy resources.

This requires the careful consideration and design of energy-aware, signal and query

processing algorithms.

1.4 Wireless Sensor Networks Reliability Research

The research in reliability for wireless sensor networks is relatively new. Each

study on WSNs has defined reliability differently and in line with their approach. Reli-

ability can be studied as a coverage problem or as message delivery reliability problem

[23]. The message delivery reliability in turn affects the data messages transport relia-

bility, and the networking protocol control messages reliability, the later in turn affects

protocols correctness.

One way to measure reliability is to specify a data delivery probability [23]. This

is proportional to the energy cost, the higher the data delivery probability the higher

is the energy cost. This fact applies to all measures of reliability. Different types of

data streams within the same network may require different reliability measures [23]

e.g. reliability for single packet delivery as in the case of delivering aggregated data to

the sink vs. block of data delivery reliability as in the case of code update, vs. periodic

reports data reliability.

Several factors can affect the wireless link data delivery reliability. Packet loss

due to congestion was identified and studied as a factor affecting the transport layer

data delivery reliability in [75]. Therefore, congestion control is considered critical to

data delivery reliability. Other reliability publications considered link failure due to

radio frequency interferences and packet collisions as the main factor affecting data

5

delivery reliability [4], [95], [101], [78], [44]. Different solutions and techniques to im-

proving the wireless link reliability were proposed. These techniques include sending

the data message through multiple routes [44], using Erasure codes to add redun-

dancy to the data packets [44], using MAC layer retransmissions [95], [94], using MAC

layer ACK/NACK control messages [11], dynamically adjusting the transmission power

based on the channel noise conditions [3], [47], or using a mixture of the mentioned

techniques in a cross-layer fashion [41].

1.5 Problem Statement

As wireless sensor network research matures, it needs to move beyond stud-

ies that are focused on addressing the challenges of energy conservation and resource

constraints. To build trust in using these systems, more emphasis should be placed

on studying and analyzing the reliability and dependability of these systems. So far,

wireless sensor networks energy efficiency research has not taken reliability into con-

sideration as a performance parameter or as a design constraint. Currently, two focus

areas in wireless sensor networks (WSNs) research can be identified. One area is con-

cerned with optimizing the energy performance and improving network lifetime. The

second area is focused on studying the WSNs reliability problem independent of the

networking and energy performance issues.

Several mechanisms for improving the wireless communication networks relia-

bility are suggested in the literature. These include Link Layer retransmissions [78],

[86] and the use of erasure codes [71], [12], [55], [15], [60], by adding redundancy to

each message thus allowing the construction of m original messages from any received n

6

messages (given n > m). The reliability algorithms proposed in the literature observe

the importance of incorporating reliability as a parameter and constraints in wireless

sensor networks research. Nevertheless, they all failed to consider these networks’ se-

vere energy resource constraints. There is a need for practical communication protocols

solutions that are reliable and energy-efficient at the same time.

1.6 Dissertation Contributions

In this work, we propose a cross-layer, reliable wireless sensor protocol design.

The protocol introduces new energy saving techniques that consider reliability as a

design parameter and constraint. Below are the contributions of the proposed protocol

design:

• A reliable, energy-aware cross-layer protocol that benefits from the body of re-

search in the wireless sensor networks reliability and wireless sensor networks

energy conservation areas. The protocol optimizes energy consumption while

providing a reliable data delivery network.

• The proposed protocol classifies the network messages based on their type. We

outline a setting in which each message carries its own reliability requirements.

In the chosen network architecture, three message types are proposed: event

reporting, periodic reports, and infrastructure communication messages. A dif-

ferent level of importance and reliability requirement is then attached to each

message type.

7

• We propose a one-hop Hello message exchange that takes place at several different

power levels. This is needed in order to measure neighbors links power versus

reliability characteristics. Each node will store the collected statistics locally for

use in future routing and clustering decisions.

• A proposed Link Rating parameter that is used by the protocol’s networking layer

in optimizing the clusters formation. This optimization is done while observing

reliability constraints.

• An Individualized Link Power Settings algorithm. Nodes will communicate with

their cluster heads using this algorithm. This will optimize intra-cluster commu-

nication under reliability constraint.

• At the MAC layer, we propose a Dynamic Back-off algorithm. It is a random-

ized back-off algorithm that is applied to adjust the back-off timer based on the

message reliability setting. The result is that in congested or heavily utilized

bandwidth, messages with high reliability requirements will have shorter back-off

times, thus increasing their chance of getting delivered. Another part of the Dy-

namic Backoff algorithm is varying the number of MAC retransmission attempts.

The number of retransmissions depends on the message type. Messages with high

reliability requirements have higher number of retransmission attempts.

• We developed simulation models for the proposed protocol and a reference pro-

tocol, APTEEN [59]. The simulation models are developed for the proposed

protocol and the reference protocol using JiST/SWANS [9], [10] simulator.

8

1.7 Thesis Organization

This work presents a cross-layer, reliable wireless sensor protocol design. The

protocol benefits from the body of research in the two areas of wireless sensor reliability

and wireless sensors energy conservation. This dissertation is organized as follows:

• Chapter 1 is the introduction. It introduces the wireless sensors and wireless

sensor networks topic. It motivates the need for energy conservation and re-

liability in wireless sensor networks research. The chapter introduces wireless

sensor networks reliability research and point out the need for bridging the gap

between energy conservation research and reliability research in wireless sensor

networks. This paves the foundation for the problem statement and our contri-

butions. These are presented later in the chapter.

• Chapter 2 covers background material about the Open System Interconnection

(OSI) networking stack model. The chapter includes survey and classification

of related work in energy optimization protocols for wireless sensor networks

(WSNs). The classification follows the layers of the OSI network model.

• Chapter 3 is a survey of reliability research in wireless sensor networks. Although

the work in reliability for WSNs is in its infancy and found to be very diverse,

an attempt is made to classify it following the OSI stack model. This is in order

to keep it inline and similar to the survey of the energy optimization research,

presented in Chapter 2.

9

• Chapter 4 presents our contribution and the design details of the proposed pro-

tocol. The chapter starts by discussing the wireless sensor networks energy op-

timization research and reliability research. This motivates the discussion about

the need to bridge the gap between the two research concentrations. The chapter

then presents the proposed protocol network architecture and gives a classifica-

tion for the network messages based on their reliability needs. A justification is

given for each message class reliability setting chosen. The first component in the

proposal, the Hello, Hello-reply messages exchange is presented next. The rout-

ing layer component along with the proposed Link Rating parameter is discussed.

The chapter then covers the MAC layer proposed Dynamic Backoff algorithm.

The proposed protocol startup and steady state operation phases are then pre-

sented. The chapter also shows a comparison between the proposed protocol

and several related wireless sensor networks protocols. Finally, the chapter con-

cludes with a discussion of cross-layer design techniques, benefits and drawbacks.

We then show the techniques that the proposed protocol uses to benefit from

cross-layer optimization while avoiding the cross-layer drawbacks.

• Chapter 5 presents the performance evaluation. We start with presenting the

simulation tool and the tool selection criteria. Then, we discuss the upgrades

applied to transform the selected tool to fit our performance evaluation require-

ments. The chapter also includes the simulation scenarios, and performance

evaluation results and analysis.

10

• Chapter 6 presents our conclusions for this dissertation and present future work

and extensions to this research.

11

Chapter 2

CLASSIFICATION OF WIRELESS SENSOR NETWORKS

ENERGY OPTIMIZATION PROTOCOLS

This chapter presents background material about the Open Systems Intercon-

nection (OSI) model. The chapter includes a literature survey and classification of the

energy optimizations protocols proposed for wireless sensor networks. The OSI model

stack layers are used for the classification. A survey and discussion of cross-layer opti-

mization is presented at the end of the chapter.

2.1 The Open Systems Interconnection (OSI) Network Stack

The Open Systems Interconnection (OSI) model is a standard developed by the

International Organization of Standards (ISO) for how to transmit messages between

any two telecommunicating points in a network [23], [64]. The standard defines seven

layers of functions that take place at each end of a communication. Each layer is

responsible for a number of logical steps that it implements. Several performance

parameters of the OSI stack for wired networks have been optimized. These parameters

include latency, fairness and throughput.

In the OSI model, the communication process between two points in a network

is divided into seven layers: Application, Presentation, Session, Transport, Network,

12

Medium Access Control, and Physical layers [23]. An advantage of this view is that

the complexity of the communication process is also divided among the different layers

making the implementation of such systems manageable. The programming and hard-

ware that furnishes the seven layers, also known as the network stack, is usually found

partly in the computer operating system, in several stand alone applications such as

Web browsers, and in the network firmware and hardware interfaces that are common

parts of any computer system.

The above discussion presented the well-known OSI stack. It describes a fixed,

seven layer stack for networking communication protocols. Similarly, there is another

layered stack protocol, which is the simpler five layer stack model, also known as the

TCP/IP protocol stack shown in Figure 2.1. There are lots of similarity between the

two protocols since they attempt to define the same communication process, but the

definition of the different layers are some what different. Wireless sensors network

stack has more in common with the TCP/IP stack. These five layers are summarized

below:

2.1.1 Application Layer

The application layer sits at the top of the communication stack. It generates

the data that will be sent out or it will be the entity that ultimately receive and

decodes the data. At this layer the communicating partners are identified, quality

of service is defined and identified, data encryption and decryption is performed, and

user authentication and privacy issues are considered. In the seven-layer protocol stack

model, this layer is further divided into the presentation and session layers.

13

Figure 2.1: OSI five layer protocol stack model.

2.1.2 Transport Layer

The transport layer provides transparent data transfer between hosts. It is re-

sponsible for end-to-end error recovery and flow control. It is also responsible for pro-

viding a reliable, error-free communication over an unreliable communication medium

and ensuring complete data transfer. The well-known Transmission Control Protocol

(TCP) and User Datagram Protocol (UDP) are implementations of the transport layer

functionality.

2.1.3 Network Layer

The network layer performs error control, source to destination routing by in-

suring the sending of data messages in the right direction to the right destination on

14

outgoing transmissions, and receiving incoming packet transmissions. This layer is also

responsible for flow control, and data segmentation and de-segmentation. IPv4, IPv6,

and X.25 are the most commonly used implementations for this layer.

2.1.4 Medium Access Control (MAC) Layer

The MAC layer regulates the usage of the shared communication medium. Be-

fore transmitting frames a station must first gain access to the medium. For a Local

Area Network (LAN) this can be the token in a token ring network. In a wireless

network scenario the medium is the radio channel. The IEEE 802.11 R© [1] is a wireless

communication MAC standard that is widely adopted. In this standard, as a condition

to access the medium, the MAC layer checks the value of its network allocation vector

(NAV), which is a counter resident at each station that represents the amount of time

that the station accessing the channel needs to send its frames. The NAV must be

zero before a station can attempt to send a frame. Prior to transmitting a frame, a

station calculates the amount of time necessary to send the frame based on the frame’s

length and the channels data rate. The station places a value representing this time in

the duration field in the header of the frame. When stations receive the frame, they

examine this duration field value and use it as the basis for setting their corresponding

NAVs. This process reserves the medium for the sending station.

In general, contention-based medium access is implemented by the Distributed

Coordination Function (DCF), which is a random back off timer that stations use if they

detect a busy medium. If the channel is in use, the station must wait a random period

of time before attempting to access the medium again. This ensures that multiple

15

stations wanting to send data do not transmit at the same time. The random delay

causes stations to wait for different periods of time and prevents them from sensing the

medium at exactly the same time, finding the channel idle, transmitting, and colliding

with each other. The back off timer significantly reduces the number of collisions and

corresponding retransmissions especially when the number of active users increases.

For radio-based LANs, a transmitting station can’t listen for collisions while

sending data, mainly because the station cannot have its receiver on while transmitting

the frame. As a result, the receiving station needs to send an acknowledgment (ACK)

if no errors were detected in the received frame. If the sending station does not receive

an ACK within a specified period of time, the sending station will assume that there

was a collision or radio frequency (RF) interference and retransmits the same frame

again.

2.1.5 Physical Layer

The physical layer is the bottom layer of the OSI stack. It provides the hardware

means of sending and receiving data on a carrier and performs services requested by

the MAC layer.

The physical layer is the most basic network layer, providing only the means

of transmitting raw bits rather than packets over a physical data link. This layer

transmits the bit stream through the network as an electrical or electromagnetic sig-

nal. It provides bit-by-bit node-to-node delivery, signal modulation and demodulation,

equalization filtering, training sequences, pulse shaping and other signal processing of

physical signals. The physical layer determines the bit rate in bits per second (bits/s),

16

also known as channel capacity, digital bandwidth, maximum throughput or connection

speed. The physical layer also defines half duplex or full duplex transmission mode.

Since the inception of the ISO OSI layered communication stack model for the

wired Local Area Network (LAN) and Wide Area Network (WAN), the goals have been

to achieve compatibility and simplification of functional description of separate units.

The optimizations of the stack have also been in the direction of improving the latency,

quality of service (QoS), reliability and throughput matrices.

With the emergence of ad hoc and wireless networks, the OSI stack is ported

as is to this new technology. Research has been active in the study of ways to enhance

the stack to optimize it and bring it up to face the new challenges found in the wireless

communication field. Token ring protocols have been replaced by a new set of proto-

cols suitable for the wireless communication e.g. Ad-hoc On-demand Distance Vector

(AODV) [67], Dynamic Source Routing (DSR) [40], and Zone Routing Protocol (ZRP)

[33] to mention a few. In the medium access control layer, several new editions have

been introduced e.g. IEEE 802.11 and IEEE 802.15.4R© (ZigBee) [2] protocols.

2.2 OSI Layers Optimization for Wireless Sensors

The emergence of the new technology of wireless sensor networks (WSNs) in-

troduces a new set of constrains forcing the optimization of the OSI stack for yet new

parameters. New protocols and optimizations have been proposed by researchers to

address the energy and other requirements in wireless sensors. Below are the different

solutions and ideas classified by OSI layers.

17

2.2.1 Application Layer

Wireless sensor networks are known to be application specific. The nature of

message exchange between the nodes and the base station is mostly of reporting sensor

readings, which tend to be short messages. Given the vast number of applications

envisioned and already in existence for wireless sensors, along with their different traffic

patterns, it will be a challenge to achieve general optimization of the application layer

for energy performance. Yet, in several publications an indication is given to the need

for applications that have small memory footprint due to the limited storage resources

[13]. Other application characteristics that can be helpful in performance optimization

include the ability to tradeoff between energy and accuracy and dynamic adaptability

to node and network resources. The application layer may also assist the rest of the

stack layers with hints that will help them optimize their performance in a cross-layer

fashion.


The transport layer provides congestion control and end to end reliable data flow.

The TCP type transport layer protocols may not be suitable for wireless sensors since

they rely on end-to-end acknowledgments and retransmissions, which waste valuable

energy resources [88]. The requirements for the transport layer reliability can be relaxed

to event-to-sink reliability instead of node-to-sink. This is possible due to the fact that

the same event will be reported by several nodes [77]. [38] introduces the Sensor TCP

(STCP), a generic transport layer protocol for wireless sensor networks. STCP is

applicable to event driven as well as continuous reporting application communication

18

scenarios. It addresses several requirements of the transport layer and wireless sensor

networks including scalability and congestion detection and avoidance. In [97] the

PSFQ (Pump slowly fetch quickly) transport layer protocol is presented. In PSFQ

data recovery and loss detection is done on hop-by-hop basis instead of the original

end to end method.

2.2.3 Network/ Routing Layer

Energy efficient protocols for wireless sensor networks exploit the fact that these

networks are not communication networks in the classical meaning, but rather they

are distributed systems where all the nodes collaborate to perform a given task or

set of tasks. This fact can be used to trade per node fairness and other networking

qualities for designs that will yield energy efficient protocols. The networking layer

is responsible for the end to end routing and delivery of data messages. This makes

designing of energy efficient protocols in the routing layer critical since this will affect

the number of transmissions, the distance covered per transmission and the load placed

on nodes participating in the relaying of the message. For these reasons, the network

layer attracted more attention than the other layers. Some of the early work on energy

efficient wireless sensor protocols has targeted this layer [35], [52], [58], [59]. In the rest

of this section, we survey some of the proposed network protocols for wireless sensor

networks.

2.2.3.1 Low-Energy Adaptive Clustering Hierarchy (LEACH)

Perhaps the first network protocol that is specifically designed for wireless sen-

sors is the LEACH protocol [35]. The main setting that this protocol addresses is

19

that of a large number of homogeneous, resource constrained nodes monitoring the

environment and periodically sending their readings to a base station located far away

from the field as shown in Figure 2.2. The protocol achieves its power saving goals

by allowing a small percentage of the nodes, called cluster heads, to collect data from

their surrounding neighbors, aggregate that data and send a report to the base station

representing the combined readings.

The protocol avoids depleting the cluster heads energies by selecting a new set

of cluster heads at the beginning of each round. The set up overhead is assumed to

be negligible since the setup time is small compared to the rounds duration. The

protocol uses a randomized routine for each node to elect itself as a cluster head. This

routine is run locally at each node to avoid the traffic overhead of a centralized routine.

Simulation results show that LEACH can increase the network lifetime by as much as

a factor of eight compared with direct transmission. The protocol suffers from few

shortcomings including the fact that the energy level and other node resources are not

taken into consideration in the election routine. Yet, LEACH is considered the first

energy efficient protocol targeting wireless sensors, and the benchmark against which

the performance of other protocols is compared.

2.2.3.2 Power-Efficient Gathering in Sensor Information Systems (PEGA-

SIS)

PEGASIS [52] is an improvement over the LEACH protocol by introducing the

following ideas:

1. Nodes transmit only for a short distance to the closest neighbor. Each node

20

Figure 2.2: LEACH protocol configuration.

defuses its data with the data it receives before transmitting as shown in Figure

2.3.

2. Only one node reports the collected data to the base station instead of a group

of cluster heads going through the expensive transmission.

3. The leader node receives at most two messages instead of an average of 20 mes-

sages in the case of the LEACH protocol with a 100 nodes network [52].

PEGASIS achieves 100-300% energy performance improvement over LEACH.

The protocol does not specify how the leader is selected. But since this is an enhance-

ment over LEACH, one can assume that it uses the same random equation by setting

the number of cluster heads to one. In which case, issues associated with LEACH

cluster heads selection routine can be assumed to be present in PEGASIS.

21

Figure 2.3: PEGASIS protocol configuration.

2.2.3.3 Threshold sensitive Energy efficient Sensor Network Protocol/ Adap-

tive Periodic Threshold-sensitive Energy Efficient Sensor Network

Protocol (TEEN/APTEEN)

In classifying the routing protocols for wireless sensor networks, two classes can

be identified, proactive and reactive protocols [58]. The LEACH and PEGASIS proto-

cols discussed above can be considered to be proactive protocols since they periodically

send reports to the base station. Reactive protocols, in which reporting is triggered by

the occurrence of the event of interest are more suitable for time critical applications

where immediate response to changes in the sensed parameter(s) is required.

The Threshold sensitive Energy Efficient sensor Network protocol (TEEN) [58]

and the Adaptive Periodic Threshold-sensitive Energy Efficient sensor Network Proto-

col (APTEEN) [59] fall under this reactive category. Similar to LEACH and PEGASIS,

TEEN is also a hierarchical protocol. The protocol defines and uses two parameters,

22

a hard threshold and a soft threshold. The sensors are assumed to monitor the en-

vironment continuously. If the value of the sensed parameter reaches or exceeds the

hard threshold value, the node will turn on its transmitter and send a report to its

cluster head. To prevent the nodes from flooding the network with reports once the

hard threshold is reached, the nodes will send a new report only if the value of the

sensed parameter exceeds the last reported value by an amount equals to at least the

soft threshold value. The TEEN protocol offers the following features:

1. Time critical data is reported immediately to the user.

2. Data transmission occurs only if the threshold value is reached, thus substantial

energy conservation is achieved.

3. By varying the values for the hard and the soft threshold parameters, the user

has control on the network reporting behavior. The soft threshold can also be

adjusted to trade off between accuracy and energy saving.

As already stated in [58], the main drawback of this protocol is that if the

threshold value is never reached, the user will get no reports at all and will not be aware

if all the nodes in the network are dead. The above limitation of the TEEN protocol

was removed by introducing a hybrid version of the protocol, the APTEEN protocol

[59]. APTEEN defines a new Count Time (CT ) parameter that is also under the

users control. The count time is defined as the maximum time between two successive

reports. By setting values for this count time APTEEN can act as a pure reactive, a

pure proactive, or a hybrid protocol.

23

2.2.3.4 Directed Diffusion

Directed diffusion [37] is a data-centric protocol where data consists of an

attribute-value named pair. It can be considered as a reactive protocol where data

is requested by sending an interest in the named data. The protocol relies on local

communication between neighbors. To a node, a request arriving from a neighbor will

be treated as if it originated from that neighbor and no global routes between source

and sink exist. Initially, a node will flood its neighbors with its interest. Later it will

enforce the selection of minimum delay routes, or routes that have been constantly

delivering timely data. This protocol is applicable to surveillance and target tracking

applications.

2.2.3.5 Geographical and Energy Aware Routing Protocol (GEAR)

GEAR [103] is an energy aware geographical routing protocol for wireless sen-

sors. The GEAR protocol assumes that nodes are aware of their geographical location

for its operation. This can be achieved by using Global Positioning Systems (GPS) or

some localization algorithms. The GEAR protocol is suitable for applications where

the operator is interested in querying a specific geographical region. When there is a

neighbor closer to the destination, the protocol forwards the request to that neighbor.

When more than one neighbor exists that is closer to the destination, the GEAR picks

the one that minimizes some cost function. When all the neighbors are further away

from the destination, a hole is said to exist in the path and the GEAR protocol chooses

the neighbor that minimizes the cost function to forward the request.

24

2.2.3.6 Sensor Protocols for Information via Negotiation (SPIN)

The SPIN protocol [34] is defined as an application-level approach to network

communication. It introduces the use of high-level data naming (metadata) for mes-

sage exchange. For the effectiveness of using metadata in this protocol, the metadata

messages are assumed to be much shorter that the actual data messages. SPIN uses

a simple Advertise-Request-Data handshake to enable a node to send its data to only

those nodes that are interested in obtaining it.

A variation of this protocol, named SPIN-2 achieves further energy conserva-

tion by requiring nodes to monitor their energy resources and participate in the data

exchange phase only if they have adequate amount of residual energy. Simulation of

the SPIN-2 protocol shows that 60% more data can be delivered using this setting

compared to basic flooding.

2.2.3.7 Cost-effective Maximum Lifetime Routing (CMLR)

The CMLR protocol [36] identifies a cost function and a maximum lifetime

function and attempt to select a route that minimizes the first function and maximizes

the second one. The authors argue that while the path selected will not be the one

with the least cost function or the maximum lifetime one, it will be the route that will

attempt to optimize both.

2.2.4 MAC Layer

As stated in the previous section, the networking layer is responsible for the

end to end routing and delivery of data messages. Designing energy efficient protocols

25

in the routing layer is important since this will affect the route selected, number of

hops per message, the distance covered per transmission, and the load placed on nodes

participating in the relaying of the data. At the other end, the MAC layer is responsible

for per hop transmission between neighboring nodes. For this reason, and similar to

the network layer, the MAC layer attracted significant attention.

2.2.4.1 Sensor MAC (SMAC)

The first protocol that addresses the energy problem at the MAC layer is the

SMAC [102]. SMAC identifies the sources of energy waste at the MAC layer as being

due to the following four factors:

1. collision

2. overhearing

3. control messages overhead

4. idle listening.

To reduce the effect of idle listening, SMAC introduces the concept of periodic

listen and sleep cycles as shown in Figure 2.4. Nodes follow a sleep and listen schedule

that synchronizes them together. SMAC also attempts to address the problem of con-

trol messages overhead by reducing the number of control messages needed for data

exchange between any sender and receiver pairs. For overhearing and collision issues,

SMAC borrows from the IEEE 802.11 [1] medium access standard. The standard de-

fines a pair of control messages, Request-To-Send and Clear-To-Send (RTS/CTS) for

26

initiating communication between sender and receiver. SMAC requires all nodes hear-

ing either or both RTS/CTS messages to refrain from accessing the medium to avoid

collision. For overhearing, the nodes use the network allocation vector (NAV) concept

introduced in the IEEE 802.11 standard. In this vector, a node will store the duration

of time that a communication between its neighbors will take. This time duration can

be obtained from the RTS or CTS messages that the node overhears. The node can

then switch off its radio and go to sleep for the duration of time while its neighbors are

using the channel. To achieve these energy savings the trade-offs introduced by SMAC

are increased delays, and compromised per-node fairness.

Figure 2.4: S-MAC periodic listen and sleep schedule.

2.2.4.2 Delay MAC (DMAC)

The delay problem introduced by SMAC is partially solved in the D-MAC [1]

protocol by exploiting the structure of data gathering trees. It solves the message

forward interruption by adding an offset to each nodes schedule. This offset depends

on the nodes depth within the forwarding tree as shown in Figure 2.5.

2.2.4.3 TMAC

S-MAC is further improved by using a variable length active period in the TMAC

protocol [91]. TMAC reduces idle listening by transmitting all queued messages in

27

bursts of variable length and going to sleep directly afterwards. During active time

the node will keep listening or transmitting and will go to sleep before the end of the

active period if no further activation events are heard within a defined Activation Time

period (TA).

Figure 2.5: DMAC staggered listen and sleep schedule.

2.2.4.4 WiseMAC

In the WiseMAC protocol [26], a node will wake up regularly for a very short

period to sense the medium. If no activity is detected in the channel, the node will

go back to sleep immediately until the next sampling time. The nodes sampling times

are not synchronized together. If a node finds the medium busy, it stays awake to

receive the transmitted data. If a node has data, it will precede its transmission with

a preamble of length equal to or greater than the network sampling period. The

advantage of using this scheme is that at low traffic levels, nodes will only wake up for

very short time at each sampling period. The disadvantages are the high transmission

cost of the preamble signal, and that all nodes hearing the preamble must stay awake

28

to hear the data transmission even if it was not meant for them. To minimize this

transmission cost, WiseMAC requires the nodes to keep a list of their neighbors and

their next wakeup times. Then a transmitting node can start the preamble signal

just ahead of the receivers wakeup schedule keeping the preamble transmission to a

minimum.

2.2.4.5 Group-based Medium Access Control (GMAC)

GMAC [14] is a cluster-centric, reservation based MAC protocol. Each frame

cycle is divided into a contention period and a contention-free period. A gateway

node collects all transmission requests from its members in the form of a Future-

Request-To-Send (FRTS) control messages. The gateway then schedules the nodes that

submitted requests for transmission during the contention-free period. The gateway

node is responsible for storing the transmission request, schedule the transmission time

slots, collecting the data messages from its members, and forwarding all the traffic out

of the cluster.

To avoid the depletion of the gateways battery, the GMAC protocol uses the

Resource Adaptive Voluntary Election (RAVE) scheme to periodically elect a new

gateway node. The RAVE is a self election contention back-off algorithm that takes

into consideration the nodes available levels of energy and other resources.

The algorithm is based on the batterys voltage range, which can be used as an

indication of residual energy. The voltage ranges are divided into four different levels

high, medium, low, and minimum as shown in Table 2.1. The node will set a Battery

Power Level parameter according to its battery level. This parameter is then used to

29

calculate a ’Resource Level’ (RL) for the node that can have one of four possible values

0, 1, 2, and 3 as shown in Table 2.2. Based on the nodes RL value, its cluster head

contention back-off can be dynamically adjusted using the following equation:

ElectionBackoff = Random(27) + (RL ∗ 128) (2.1)

The main advantage of using the above settings and equation is that nodes with better

energy resource have higher chance of becoming cluster heads due to shorter contention

period.

A potential limitation of the GMAC protocol is that a node may be forced to

miss its allocated time slot and contend using FRTS message in the next contention

round. This will happen if another node belonging to a different gateway and within

interference distance from the node uses the channel during that time slot for its

own transmission. This scenario can result in excessive delays and high probability of

collisions in densely deployed networks or in applications characterized by high network

traffic.

Table 2.1: RAVE battery resource level.

Battery power Power level Voltage rangelevel nomenclature00 HIGH 2.6 < power ≤ (3.0-3.6)01 MEDIUM 2.4 < power < 2.610 LOW 2.1 < power < 2.411 MINIMUM power ≤ 2.1

30

Table 2.2: RAVE Contention back off.

Resource level (RL) Election contention backoffElectionBackoff = Random(27) + (RL ∗ 128)

0 HIGH 0 to 127 slots (0 ms to 2 ms )1 MEDIUM 128 to 255 slots (2 ms to 4 ms)2 LOW 256 to 383 slots (4 ms to 6 ms)3 MINIMUM 384 to 511 slots (6 ms to 8 ms)

2.2.4.6 Traffic-Adaptive Medium Access Protocol (TRAMA)

The TRAMA protocol [14] is similar to GMAC in that the communication

channel is divided into frame cycles. Each frame is divided into a random access (con-

tention) period and a scheduled access (contention-free) period. The scheduled period

is divided into time slots. Nodes compete during the random access period to reserve

slots for their data transmission during the scheduled access period. To guarantee a

collision-free transmission, the TRAMA protocol uses the Neighbor Protocol (NP), the

Schedule Exchange Protocol (SEP), and the Adaptive Election Algorithm (AEA) to

obtain and exchange one and two hop information and schedules. Nodes with no data

to send will switch off their radios and go to sleep to conserve energy.

2.2.5 Physical and Radio Layer

At the physical and hardware level the focus in Complementary metaloxidesemi-

conductor (CMOS) circuit design and optimization is shifting from faster switching cir-

cuits to ones that are optimized for power consumption. The work in [70] summarizes

the challenges facing low power design for WSNs as:

1. Design of low power low cost transceiver.

31

2. Low power sensing and processing unit design.

3. Energy efficient modulation schemes and strategies to overcome signal propaga-

tion effects.

Several projects focused on the radio component and the design of energy effi-

cient transceivers [17], [65], [21], [19], [27], [22], [45], [99], [81], [72], [76] [43], [51], [5],

[42]. In [72], the power consumption of the sensing and detection technique used is

discussed. An example is given where the piezoresistive sensor will draw a large cur-

rent while a capacitive one will not. [81] Discusses hardware designs that use several

operation states to conserve energy. It also presents available hooks in hardware for

power management, and CPU clock-down under OS control. In [72] power consump-

tion was considered as a design constraint for motion sensors for physiological activity

monitoring. Work in [76] and [43] cover a low power Analog/Digital converter for wire-

less sensors and a low power hardware encryption circuit, respectively. [51] And [5]

proposed design methodology and architectures for low power sensor design.

Other research projects proposed the redesign of the physical and radio layer

parameters for energy optimization. [42] Discusses low power algorithms for source

coding. In [74] results are shown for packet size optimization for energy efficiency

under given communication channel characteristics. [18] Proposed selection of radio

parameters at design time with the goal of optimizing the one hop transmission range

for energy efficiency.

32

2.2.6 Summary of OSI Layers Optimization Techniques for Wireless Sen-

sor Networks

In Table 2.3, we summarize the techniques used at the OSI stack layers and the

impact of each layers optimization on energy efficiency.

Table 2.3: Impact of layer optimization techniques on energy efficiency.

Layer Optimization Techniques Impact

Application Dynamic adaptability to resources. Tradeoff between energy and accuracy Low

Transport Loss detection and data recovery on hop by hop instead of end to end. Low

Event-to-sink reliability [64], [39]

Network Clustering. Subset of nodes participating in data transmission. High

LEACH [35], PEGASIS [52], TEEN/APTEEN [58] [59],

Directed Diffusion [37], GEAR [103], SPIN [34], CLMR [36].

MAC Low power (Sleep) Cycles. SMAC [102], DMAC [54], High

TMAC [91], WiseMAC [26], GMAC [14], TRAMA [70]

Physical Energy efficient hardware design. Optimize radio parameters, data Medium

packet size, and transmission range at design time for energy efficiency

[17], [65], [21], [19], [27], [22], [45], [99], [81], [72], [76] [43], [51], [5], [42]

2.3 Cross-layer Optimization

One of the main design philosophies of the OSI layered architecture model is to

provide a well-defined functionality and interfaces for each layer. This allows changes in

the technology and allows designs in any individual layer to be transparent to the rest

of the stack [87]. The OSI model has been widely adopted for wired networks design.

Although arguably not optimal for wireless communication [30], the OSI model has

been migrated unaltered to wireless ad hoc systems design.


33

and the sharing of information with the goal of improving the overall system perfor-

mance. It has been used in ad hoc wireless systems to improve throughput, latency,

and quality of service (QoS) [30], [69]. This Section discusses the importance of cross-

layer design and optimizations for wireless sensor networks. Some of the techniques

proposed and their advantages and drawbacks are presented.

2.3.1 The Need for Cross-layer Optimizations in Wireless Sensor Networks

A summary of the benefits of cross-layer designs for wireless sensor networks

was presented in [62]. The reasons behind the need for these cross-layer improvements

were given as:

• The stringent energy, storage and processing capabilities of the sensor nodes

necessitates such approach.

• There is a significant overhead associated with the layered protocols resulting in

high inefficiency.

• Some recent empirical studies necessitates that the properties of low power ra-

dio transceivers, which is common to wireless sensors, and the wireless channel

characteristics be considered in protocol design.

• The event-centric nature of wireless sensor networks requires application-aware

communication protocols.

34

2.3.2 WSNs Cross-layer Energy Balance and Energy Performance Analysis

2.3.2.1 Cross-layer Energy Balance

The observation that minimum-energy routing can often unfairly penalize a

subset of the nodes was made in [68]. As noted, most efforts in this direction were

targeted towards wireless ad-hoc networks and were often not portable to wireless

sensor networks. The authors then move to proposing a cross-layer energy-balancing

scheme for wireless sensor networks. This involves using a cost function that uses

hardware information such as remaining energy, channel quality, and the number of

hops routing layer metric. The success of this approach relies on the existence of a

solid MAC layer protocol that minimized in-network interference.

The reliance on a MAC protocol that can provide interference-free communi-

cation was again present in [57]. Here, the focus is on a cross-layer design for the

computation of optimal transmission powers, rates, and link schedules that maximize

the network life. The proposed algorithm alternates between link scheduling and the

computation of optimal transmission rates and powers. The authors argue that using

transmission schemes that have the following characteristics can increase the networks

lifetime:

• Multihop routing. In wireless communication, the transmission power falls off as

the mth power of distance, with 2 < m < 6. Hence, short, multihop transmission

is preferred to long range transmissions

• Load balancing. This is necessary to avoid creating hotspots where some nodes

will die quickly causing the network to fail

35

• Interference control. Links that interfere with each other should be schedule at

different times to reduce the energy required by these links to overcome interfer-

ence

• Frequency reuse. Weakly interfering links should be scheduled simultaneously.

Drawbacks: The effectiveness and suitability of the schemes introduced in [68]

and [57] for wireless sensor networks are questionable. The proposed designs rely on the

availability of a MAC protocol that can provide interference-free communication. This

implies using TDMA type protocols, which may not be a good fit for large scale, densely

deployed wireless sensor networks. An energy performance survey of the different

wireless sensor networks MAC protocols was carried out in [32]. A cross-layer analysis

that covers the medium access control (MAC) and the radio transceiver that works

in union were given. As we just pointed above, all the protocols in the survey use

CSMA/CA in their implementation. TDMA-type MAC protocols are rarely suggested

for use for wireless sensor networks.

2.3.2.2 Cross-layer Energy Performance Analysis

In [53], the authors study the energy performance of wireless sensor networks

as a function of resource allocation for a detection system. The connection between a

single center and the fusion center is modeled as a two-state continuous time Markov

chain. The model takes into consideration channel physical parameters and link layer

message delay and message loss probabilities.

Finding the optimal number of network clusters that gives the best energy per-

formance in cross-layer manner was studied in [98]. The number of cluster heads is

36

factored into the energy minimization problem that provides a scheduling policy that

integrates the physical, MAC and the routing layers.

2.3.3 Multi-layer Cross-layer Optimization Techniques

In addition to cross-layer protocol designs that focus on pair-wise layer inter-

action, there are other more general approaches that target three or more layers. An

example is [57], which proposes a protocol that minimizes transmission power, trans-

mission rate, and link schedule for a TDMA-based wireless sensor networks protocol.

Another example is the joint scheduling algorithm proposed in [92], where the nodes

form distributed on-off schedules for each flow in the network while routes are estab-

lished such that the nodes are only awake when necessary. An extreme approach was

proposed in [28], in which the traditional OSI layer architecture is thrown away and a

new communication system is redesigned from the ground up for wireless sensor net-

works. A unified cross-layer module (XLM) that uses the receiver-based routing [82],

[108], [107] was introduced. The XLM module uses this in addition to received based

contention, local congestion control, and distributed duty cycle operations in order to

realize efficient and reliable communication.

A multi-layer framework for cross-layer design that spans several levels was

proposed for wireless ad-hoc and wireless sensor networks in [73]. The framework

accepts a set of possible routes as input and uses feedback information from the physical

and MAC layers to measure how well each of the routes meets the following objectives:

• The nodes constituting the path having high remaining battery energy.

• The route is power-efficient.

37

• Reliable packet delivery.

• The route is stable in respect to route maintenance and connection disturbance.

The drawback of the above approach is that it relies on the availability of multi-

ple routes between source and destination. This may not be applicable to hierarchical

network architectures commonly proposed for wireless sensor networks.

2.3.3.1 Cross-layer Optimization Using Forward Error Correction

As a promising technique, the effectiveness of using forward error control (FEC)

schemes for constructing channel-aware routing was studied in [96]. The low power

communication constraints of wireless sensors magnify the effects of the wireless channel

leading to error-prone links. The effect of multihop routing and the broadcast nature

of the wireless medium were investigated to drive the equations governing the energy

consumption, latency, and packet error rate of error control scheme. As a result, cross-

layer analysis considering routing, medium access and physical layers was devised.

Forward error control (FEC) coding improves the error resilience by sending redundant

bits through the wireless channel. In this case, lower signal-to-noise (SNR) values can

be supported to achieve the same error rate as an uncoded transmission for the same

power cost. In a multihop network, the advantage of incorporating FEC can also

be used to construct longer hops. In order to realize this hop length extension, the

authors propose a channel-aware routing algorithm in which the next hop is determined

according to the received signal-to-noise ratios (SNR) of the possible links.

38

2.3.3.2 Cross-layer Optimization Using Feedback Optimization Agents

An Optimization Agent framework for cross layer design was proposed in [87].

As defined, this optimization agent can be thought of as a stack-wide database where

each layer can deposit essential parameters that define its current working conditions.

Other layers can access these data and use the information to tune and optimize their

performance accordingly. The framework is to provide and control the exchange of

information between the different layers to improve the systems performance. The

authors argue that when designing wireless networks, the wireless channel has some

unique characteristics that need to be taken into consideration. The broadcast nature

of the radio communication, and the signal propagation that is affected by fading, at-

tenuation, transmission power, and the rapid signal degradation with distance between

sender and receiver. The authors also point out that in the layered approach, each layer

is to offer certain services to the next higher layer. This provides a level of transparency

and reduces the complexity by splitting the network into smaller manageable modules.

Such network design helps to provide easy standardization, inter-layer interoperability,

and peer-to-peer relationship among the different networks and equipments. It also

promotes adaptability at various layers based on information exchanged. Neverthe-

less, such design requires careful consideration to avoid unintentional and undesirable

consequences, e.g. deadlocks and loops.

The framework proposed in [87] is shown here in Figure 2.6. The framework

relies on the following concepts: at the physical layer channel quality estimation is

performed to obtain the signal-to-noise ratio (SNR) of a link and the data is used

to select the data rate. This in turns affects the transmission delay. At the network

39

layer, the routing protocol then makes decisions based on the delay associated with

each link. The framework includes the proposed optimization agent (OA) component

to facilitate the interaction between the different protocols and serves as a repository

or a database where essential information provided by the different layers are kept

and used as an aid in meeting the performance optimization objectives. This idea of

cross-layer information sharing was previously introduced in [50] but as a design time

tool to support adaptability and optimization for ad-hoc networks as shown in Figure

2.7.

2.3.4 WSNs Cross-layer Design Challenges and Open Research Issues

The authors in [62] point out that a systematic methodology to accurately model

and leverage cross-layer interactions is still missing. Several studies advocate that

communication protocols for wireless sensor networks need to be redesigned consid-

ering the wireless channel characteristics. In several conservative cross-layer projects,

only the cross-layer interaction is considered, where the traditional layered structure

is preserved, while each layer is informed about the conditions of other layers. As

an example, an experimental study on the effect of the communication channel on a

simple protocol such as flooding was investigated through testbed experiments in [29].

Accordingly, the broadcast and asymmetric nature of the wireless channel resulted in

a different performance than that predicted by using the disk graph models.

2.3.5 Cross-layer Design Challenges

The study in [62] concludes with some precautionary guidelines in cross-layer

design similar to the ones introduced in [30]. The improvements gained in performance

40

Figure 2.6: Cross-layer optimization framework [87].

come at a price. This includes:

• Decreased architecture modularity.

• Loss of the decoupling between design and development.

• Cross-layer designs may be hard to debug, maintain or upgrade.

• The interdependencies introduced need to be carefully considered and evaluated

to avoid the non-trivial problem of system’s instability.

2.3.6 Cross-layer Open Research Issues

The survey in [62] also pointed out some open cross-layer research issues. First,

there is still much to be gained by rethinking the protocol functions of network layers

in a unified way to provide a single communication module. The cross-layer approaches

recently emerged still depend on event communication between the layers that considers

41

Figure 2.7: Adaptive cross-layer design operation [18].

the transport, networking MAC functionalities with physical layer. Some open issues

towards the development of systematic techniques for cross-layer design include:

• Identifying adequate utility functions: Functions that represent the global

design objectives such as minimum energy consumption and maximum network

lifetime. These functions need to converge allowing for the location of a global

optimum operational point in an efficient manner

• Improved understanding of energy consumption: Existing studies focus

on optimizing functionalities at the different layers with the overall objective of

minimizing the energy consumption. Hence, further studies are needed to develop

modules and methodologies to solve the energy-oriented problems

• Accurate delay modeling: There is a need for accurate delay modeling due to

the interaction between the different layers. This is particularly important in the

42

design of cross-layer protocols for real time and delay-bonded applications such

as in monitoring and tracking applications

• Connectivity with realistic physical layer: Several studies concluded that

the effect of the physical layer and the impairments of the wireless channel on

the design and operation of the upper layers are not negligible. Accordingly, new

analytical models that take into consideration fading and other link connectivity

issues are needed.

• Cross-layer network simulators: Existing discrete-event network simulators

[61], [63], [93], [16], [48] may not be suitable for evaluating cross-layer designs.

The inner structures and implementation of these simulators are tightly coupled

to the layered architecture. Implementing a cross-layer solution on these simu-

lators may run into a non-trivial task. Hence, there is need for new simulators

that are based on a new paradigm so as to ease the development and evaluation

of cross-layer solutions

2.4 Summary

This chapter presented the Open Systems Interconnection (OSI) model and its

variant, the TCP/IP stack model. The chapter surveyed the energy optimizations

techniques proposed for wireless sensor networks classified based on the OSI stack

layer they aim to optimize. Finally, a discussion of cross-layer optimization was pre-

sented. The benefits, challenges and drawbacks of cross-layer designs were outlined.

43

The discussions here indicate that cross-layer design and optimization is critical to suc-

cessful WSN protocol design. The next chapter addresses reliability in wireless sensor

networks.

44

Chapter 3

RELIABILITY IN WIRELESS SENSOR NETWORKS

This chapter is a survey and classification of the wireless sensor networks reliable

protocols. The classification follows the OSI stack model that was explained and used

in Chapter 2. In this Chapter, we aim to highlight that fact that past research in

reliability for wireless sensor networks is in isolation from energy conservation research.

3.1 Importance of Wireless Sensor Networks (WSNs) Reliability

As the wireless sensor networks research matures, it needs to move beyond

studies that are focused on realizing these networks, e.g. studies that address the

challenges of energy conservation and resource constraints. To build trust in using these

systems, more emphasis should be placed on studying and analyzing the reliability and

dependability of WSNs. So far, wireless sensor networks energy efficiency research

has not taken reliability into consideration as a performance parameter or as a design

constraint.

WSNs reliability research is still in its infancy. There is no unified definition for

wireless sensors’ reliability and each work has defined reliability differently and in line

with their approach. Figure 3.1 shows a pie chart of the classification carried by [20]

of numerous IEEE and ACM publications and proceedings on wireless sensor networks

45

over the past few years. It shows that while routing and MAC layer have attracted

their share of attention, research in wireless sensor networks reliability acquired only

5% of those publications.

The OSI stack was instrumental in organizing the studies for WSNs energy con-

servation research and protocols. A similar organizing framework does not exit for

WSNs reliability protocols and WSNs reliability evaluation research. Reliability can

be studied as coverage problem or as message delivery reliability problem [100]. The

message delivery reliability in turn affects the message transport reliability, and the

networking protocol control messages reliability. The later affects protocol’s correct-

ness. In this work, we concern ourselves with the message delivery reliability challenges

and solutions.

Figure 3.1: Classification of the related work in WSNs [20].

46

3.2 Classification of WSNs Reliable Protocols

A previous survey work [100] discussed WSNs reliability challenges and intro-

duced some of the transport protocols that addressed WSNs reliability. The protocols

presented were classified according to their message data stream type as single packet

vs. block of packets vs. periodic stream of packets. The limitation of this classification

approach is that it leaves out a considerable number of WSN reliability studies that

approached the problem from a more abstract level and did not necessary fall under

any of the categories in the above classification. We present more general classifica-

tion terms that attempt to organize the WSNs reliable protocols under categories that

follow the OSI stack layers.

One way to measure reliability is to specify a ‘data delivery probability’ [100].

Higher data delivery probability requirements imply higher energy cost. This is true

regardless of the definition used for reliability. Different types of data streams within

the same network may require different reliability measures [100] e.g. single packet

delivery reliability as in the case of delivering aggregated data to the sink, block of

data delivery reliability as in the case of code update, and periodic reporting data

reliability. In this Section, we attempt to classify the reliable protocols proposed for

WSNs using the OSI stack layer as a classification platform similar to the classification

carried out in Chapter 2.


In [78], the authors argue that while node redundancy, inherent in WSNs in-

crease the fault tolerance, no guarantee on the reliability levels can be assured. Further,

47

the frequent communication failures within WSNs impact the network’s reliability over

time and make it more challenging to achieve a desired reliability. Another issue is that

flooding of bursty raw data causes broadcast storms, which can result in collisions,

contentions and power wastage. In-network processing is an optimization that reduces

redundancy resulting in fewer collisions, contentions, and enhances responsiveness. In

order to model the reliability of data transport, all the operations on data starting from

its generation to dissemination, aggregation and transmission need to be considered.

A framework for modeling reliability in WSNs is currently missing [78]. Such a

framework will be useful in classifying existing transport protocols and in comparing

their reliability performance. Next, we present examples of reliable Transport layer

protocols proposed for wireless sensor networks.

3.2.1.1 Event-To-Sink Transport (ESRT) Protocol

The first reliable transport protocol proposed for wireless sensor networks is the

Event-To-Sink Transport (ESRT) protocol. The concept of event-to-sink reliability is

introduced in [75] as an alternative to the classical source to destination reliability.

This is more applicable to WSNs since data flows from nodes to sink are loss-tolerant.

Event detection and the reliable relaying of information to the sink is what matters in

determining the reliability and successful operation of the network. This is regardless

of how many sensors did successfully deliver information about the detected event. The

event radius defines a circle around the event within which all the enclosed nodes will

be able to sense (detect) the event as shown in Figure 3.2.

ESRT is a centralized protocol that runs only on the sink and thus leveraging its

48

abundance of computing and power resources and relieving the resource constrained

nodes. In ESRT, congestion control is identified as an important factor for reliable

data flow since packet loss due to congestion can impair event detection at the sink.

The reliability requirements are determined by the application layer. The transport

layer reliability is defined by a reliability factor that is the ratio between the number

of received packets at the sink to the optimal number of packets that is required for

reliable event detection. Ideally, the ratio should be maintained as close as possible to

one. The reporting frequency is defined as the number of reports that the nodes need

to generate per unit time to achieve required event detection reliability. This factor is

calculated and broadcasted to all nodes by the sink. The protocol operation also relies

on congestion detection. To do this the sink relies on the nodes setting a congestion

flag bit on their reply messages. A node will monitor its buffer fullness and the rate at

which the buffer is getting filled. The congestion flag bit will be set if the node predicts

that it will experience a buffer overflow during the next reporting period.

Figure 3.2: ESRT Event Radius [75].

The ESRT protocol identifies five network states as shown in Figure 3.3, these

states are:

49

1. 1 (NC, LR) not congested, low reliability.

2. 2 (NC, HR) not congested, high reliability.

3. 3 (C, HR) congested, high reliability.

4. 4 (C, LR) congested, low reliability.

5. 5 (OOR) optimal operating region.

The protocol will use the reporting frequency to control the transition of any of

the states to the OOR state as shown in Table 3.1.

Figure 3.3: EESRT Five reporting regions [75].

3.2.1.2 Reliable Multi-Segment Transport Protocol

The Reliable Multi-Segment Transport (RMST) protocol, a transport layer pro-

tocol design to be used with directed diffusion was presented in [86]. The authors point

out that the emphasis on energy conservation in sensor networks implies that poor

50

Table 3.1: ESRT protocol operation in each of the five states.

Network State (Si) ESRT Action(NC,LR) Multiplicatively increase f. Achieve required reliability as soon

as possible(NC,HR) Decrease f conservatively. Cautiously reduce energy consumption

so as not to compromise reliability(C,HR) Decrease f aggressively to state (NC,HR) to relieve congestion,

then follow actions in (NC,HR)(C,LR) Decrease f exponentially. Relieve congestion as soon as possibleOOR f remains unchanged

paths should not be selected during route discovery. Nor should they be artificially

bolstered via MAC Automatic Repeat Request (ARQ) mechanisms. Given the high

wireless link loss rate (between 2% - 30%), the authors address the tradeoffs between

implanting reliability in the MAC layer (i.e. hop-to-hop) vs. in the transport layer

(end-to-end). Several design choices were explored. MAC layer ARQ refers to the

hop-to-hop recovery of missing or erroneous packets. This includes using RTS/CTS

and ACK control messages. If the design is using no ARQ, the transmissions do not

employ MAC layer reliability mechanisms. In this mode reliability is deferred to the

transport or application layer. There are several benefits to this approach including:

• Significant amount of overhead associated with the RTS/CTS and ACK exchange

can be avoided

• Routing protocols attempt to select high quality paths for data transmission

The performance of caching and non-caching designs was also discussed in [86].

In caching, each node in the path caches the fragments that make up a larger data

51

entity. When a node senses a missing fragment, a repair request is sent to the next hop.

If the requested fragment is in the neighbor’s local cache a response is sent. Otherwise,

the request is forwarded towards the data source. In the non-caching design, only the

sinks can detect missing fragments and data errors.

3.2.1.3 Analysis and Classification of WSNs Reliable Transport Protocols

Shaikh, et al. in [78] present a definition for data transport reliability as that

the sink detects the phenomenon of interest within an application-specific time bound.

Existing WSNs data transport protocols were classified into two categories: end-to-end

(e2e) and event-to-sink. The RMST protocol [86] was used as example for e2e. It relies

on selective NACK to detect message loss at the sink. The ESRT protocol [75] was used

as an example for event-to-sink category. From the RMST reliability block diagram

shown in Figure 3.4, the equation for the protocol’s reliability is derived as given by

Equation 3.1. Similarly, from the block diagram for ESRT in Figure 3.5, the reliability

can be derived as shown in Equation 3.2. Using Equation 3.1, the positive impact of

increasing the number of retransmissions on RMST performance can be studied.

52

Figure 3.4: Reliability block diagram for RMST [78].

RRMST = 1(1 − RR) ∗ (1 − (RR ∗ RMLD))r (3.1)

Where r = number of retransmissions, RR is the routing reliability, and RMLD is the

reliability of message detection.

Figure 3.5: Reliability block diagram for ESRT [78].

RESRT = 1 − (1 − RR)n (3.2)

Where RR is the routing reliability and n is the number of sources reporting the phe-

nomenon to the sink.

As another example for event-to-sink reliable transport protocols, the RBC [106]

protocol was presented. The reliability block diagram for the protocol is shown in

53

Figure 3.6, from which the reliability equation can be derived as given by Equation

3.3. From Equation 3.2 and Equation 3.3, the performance of these two event-to-sink

protocols can be analyzed.

Figure 3.6: Reliability block diagram for RBC [78].

RRBC = 1 − (1 − [1(1 − RR) ∗ (1 − (RR ∗ RMLD))r])n (3.3)

Where r = number of retransmissions, RR is the routing reliability, and RMLD is the

reliability of message detection.

3.2.1.4 Improving Transport Reliability by Using MAC Layer ARQ

The authors in [78] present an analysis for the effect of MAC retry limit on

reliability. If p is defined as the probability of success for a single attempt across one

hop, and R is the number of MAC attempts, then the probability of success (Ph) with

MAC ARQ in R retries is given by the following equation:

Ph =R−1∑

p.(1 − p)i (3.4)

54

This can be simplified as:

Ph = 1 − (1 − p)R (3.5)

For H hops, the end-to-end reliability (Pe ) is:

Pe = (Ph)H (3.6)

The graph in Figure 3.7 plots the probability of message arrival vs. MAC retry

limit for 40 hops and error rate of 0.10 per hop. It is very interesting to see that for

such a high link error rate, only few retransmissions are effective in achieving a high

probability of message arrival. Figure 3.8 shows the probability of message arrival

at the sink over 6 hops when using MAC ARQ (RTS/CTS and ACK) and when not

using ARQ. The graph shows clearly that the probability of arrival drops sharply while

increasing error rate for non-ARQ, but stays high when using MAC ARQ. This shows

the effectiveness of MAC layer ARQ in combating the unreliable nature of the wireless

link.

Figure 3.7: Probability of arrival across 40 hops with an average error rate of 0.10per hop, given R retries per hop [86].

55

Figure 3.8: Probability of arrival across 6 hops [86] .

3.2.2 Network Layer

3.2.2.1 Reliable Routing Using Graph Theory Analysis

Graph theory architectural models are used intensively in computer networks to

construct mathematical representations for these networks. In [56], a reliability analysis

using graph enumeration is presented. Reliability is defined as the probability that the

network will function. The network is modeled as a graph G = (V, E) composed

of elements that can fail statistically independent of one another and their failure

probabilities are known. Failure states enumeration, minimum paths enumeration, and

cuts enumeration between source and destination are used to calculate data delivery

probability. From the analysis it is clear that this is a source-to-sink communication

scenario.

The problem of computing a measure for reliability and the maximum message

delay in WSNs was addressed in [3]. Reliability is defined as the probability that there

exists an operational communication path between the sink and at least one operational

sensor in the target cluster (target area). The failure of one or mode nodes may not

56

cause the data sources to be disconnected from the data sinks. This can be considered

an event-to-sink reliability scenario. The network is modeled by a probabilistic graph

G = (V, E), where every node in the network is represented by a node in V . Each

node v in V has an associated operational probability Pv. An edge exists between two

nodes if they are within communication range from each other. The network and the

corresponding graph model are shown in Figure 3.9 (a) and Figure 3.9 (b), respectively.

Figure 3.9: (a) A wireless network, (b) The Graph model.[3].

3.2.2.2 Multiple Routes and Erasure Codes Reliable Protocols

A simple network as shown in Figure 3.10 was used to derive the reliability

definition used in [3]. The components reliabilities are as shown in the figure. From

the graph, the network reliability is derived as:

Rel(G) = PB(PH1(1 − (1PS1)(1PS2)) + (1PH1)PG.PH2.PS2) (3.7)

In the reliability analysis given, many networking factors were eliminated from

playing a role in the reliability calculation. An example is the effect of MAC layer

retransmissions on link layer reliability.

57

Some of the surveyed work introduced mechanisms for improving WSNs reliabil-

ity while observing their resource constraint. [44] Starts by introducing the equation:

Number of packets received = Psuccess ∗ Number of packets sent (3.8)

The per packet delivery success probability (Psuccess ) can be obtained through

empirical studies. This value is fixed and nothing more can be done to improve it.

The technique used here is to increase the number of packets sent sufficiently enough

so that all the data is received. The following mechanisms for improving the network’s

reliability were suggested:

• Link Layer retransmissions

• Use of erasure codes [71], [12], [55], [15], [60] which adds redundancy to each

message thus allowing the construction of m original messages from any received

n messages ( given n > m) as shown in Figure 3.11

• Providing alternative routes (multiple routes) to replace failing links

Figure 3.10: Network Graph [3].

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Desirable properties for erasure codes algorithms used in wireless sensor net-

works were pointed out in [46] as:

• The ratio mn

needs to be dynamic since it is dependent on the current channel

error conditions

• There should be no restriction on the packet length

• Encoding and decoding should be inexpensive in their memory and processing

requirements

The authors point out limitations with traditional erasure codes [71], [12] used in

[44] such as Reed-Solomon codes since they fail to satisfy the above properties.

A suggested solution is to use the new classes of erasure codes such as Luby

transform [55], Raptor [15], and Online codes [60] that have better performance

for WSNs.

Figure 3.11: Erasure Code Mechanism [44].

Similar to the technique above, [24] suggests improving reliability by sending

the data message through multiple paths. The algorithm tries to find k disjoint paths

59

between source and destination and adding redundancy to each packet so that only

E (E < k) packets need to reach the destination for the original message to be con-

structed. The algorithm assigns a ’reputation coefficient‘ to each path based on its

past performance. The degree of redundancy added to each message depends on the

number of paths (k), and on the reputation coefficients of the available paths. The al-

gorithm and the associated analysis are built around the notion of source to destination

reliability.

Limitations of Erasure Codes and Multiple Routes Reliable Protocols: The

limitations with using erasure codes and multiple routes to improve reliability in WSNs

are the following:

• They depend on the existence of multiple routes between source and destina-

tion. This may not be applicable to hierarchal topologies that are the typical

architectures for large scale wireless sensor networks

• The surveyed Erasure Code protocols failed to study the energy consumption

overhead of adding redundant bits to each packet

• There is an energy overhead introduced by transmitting more packets than needed

to construct the original message

3.2.2.3 Reliable Routing Using Link Connectivity Statistics

In [101], Woo et al. state that the dynamic and lossy nature of the wireless

communication medium posses a major challenge to reliable self-organizing multi-hop

networks. This is more problematic with the simple, low-power transceivers commonly

60

used in sensor networks. To improve communication reliability, the authors propose

capturing link connectivity statistics dynamically by using efficient and adaptive link

estimators. Routing decisions should exploit such connectivity statistics.

Each node maintains a neighborhood table. The table stores link status, quality

and routing information. The link quality estimators can be very efficient in imple-

menting cost-based routing. A challenge arises that in dense networks, a node may

receive packets from more nodes than it can represent in its neighborhood table. The

challenge is for a node to decide in which nodes it should invest its limited neigh-

borhood table resources to maintain link statistics. The problem is that if a node is

not in the table, there is no place to record its link quality information. The authors

then attempt to develop an algorithm for neighborhood management that will keep

a sufficient number of good neighbors in the table regardless of the network density.

The problem addressed here has aspects in common with cache management and with

database statistical estimation techniques.

3.2.3 MAC Layer

The MAC layer uses a few mechanisms to improve the per-hop communication

reliability. Several reliable protocols that rely on these mechanisms have been proposed.

In this Section, we present some of these protocols classified by the MAC mechanisms

used.

3.2.3.1 Reliable Protocols Using MAC Layer Retransmission

Several studies have attempted to analyze the various WSNs parameters and

their impact on the overall network reliability and performance. As an example, MAC

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retransmission is considered to be very effective in improving reliability. Nevertheless,

it is also an expensive operation and wastes valuable resources [4]. Challenges to

achieving reliability in WSNs were summarized as being due to:

• Nature of the wireless medium and burst errors

• Resource constraints

• Algorithms to achieve reliability cannot be computationally extensive

As stated in [95], one of the major factors affecting the reliability in multi-hop net-

works is the local retransmission reliability mechanism implemented in the MAC layer.

The performance of this mechanism depends mainly on the maximum number of re-

transmissions for packet failures. The effect of this mechanism on the overall network

performance was investigated and the results show that although there is a significant

difference between the maximum number of retransmissions, Rtmax = 4 and Rtmax =

7, further increase in the retransmission limit to 10 does not have a significant effect

on the overall network reliability.

3.2.3.2 Reliable Protocols Using MAC Layer Contention Window Size

Contention is one of the major sources of packet drop. For this reason con-

tention resolution mechanisms are needed at the MAC layer. Contention resolution

is performed via contention window adjustment. A node selects its random back-off

time between (0, cw), where cw is the contention window size. This window size is

initially set to minimum and as the contention level increases in the vicinity of the

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node, the size of the contention window is increased. Hence, the current value of cw is

a representative of the local contention level.

The interaction between the contention window size and retransmission tech-

niques for improving end-to-end data delivery reliability was studied in [31]. The

authors point out that the radio link exhibits varying reliability over time, space and

from node to node. Similar to [75], where the primary source for data loss is due to

noise and environmental effects rather than congestion. The important findings in [11]

are the following:

• Link layer retransmissions are necessary for improving reliability

• A small number of retransmissions are sufficient for a satisfactory reliability im-

provement

• The cost of higher reliability is higher network overhead due to longer path lengths

and excessive retransmissions

3.2.3.3 Reliable Protocols Using MAC RTS/CTS Messages

In [94] the focus is on the reliable multihop bulk transfer of data. The authors

argue that the vast majority of research is focused on reliable and power-efficient trans-

fer of small amount of data. However, in a few WSN applications, reliable transfer of

mass data is essential. The authors present bulk transfer service that achieves reliabil-

ity by employing a simplified version of IEEE 802.11. This is achieved by RTS/CTS

handshake as shown in Figure 3.12 to protect long packet bursts and provide a simple,

63

efficient flow control by allowing only one data stream to communicate at any given

time.

Figure 3.12: RTS/CTS handshake to protect longer packets bursts [94].

3.2.3.4 Reliable Protocols Using MAC ACK Messages

The concept of event to sink reliability is used in [11]. The sink sends its query

to the network and indicates in the query if reliable delivery of the response is required,

by setting a flag in the query. The authors address the issue of a parent node failing

after receiving a message from its child. In this case, the child will not receive an ACK

and dynamic route switching algorithm will be executed. This will select a new parent

through which the data will be sent.

3.2.4 Radio and Physical Layer

The relationship between radio range and reliability was discussed in [3]. An

increase in the radio range results in an increase in the one hop reliability. The relation-

ship between radio range and power was also discussed, as radio range is proportional

to at least the square of the power. Therefore, increasing the radio range is effective

in improving the one-hop reliability, but extremely taxing on energy.

The work in [47] presents a cost-based reliable algorithm. The network tries

to set the one hop retry limit and the transmission power to a setting that minimizes

64

the power consumption metrics while meeting some reliability requirements constraint.

Here again the probability of one hop successful transmission is shown to be propor-

tional to the transmission power as given by the equation below:

P(successfulTx) = f(

Gain ∗ Tx power

Noise power

)(3.9)

The main theme in [79] is to study reliability as a coverage and connectivity

problem. Necessary and sufficient condition for a random grid network to cover a unit

square region was derived. Similarly, it provided sufficient conditions for connectivity.

The results can be used to determine the tradeoff between node diameter, reliability,

and power consumption since the radio propagation radius is directly related to the

transmission power. For a network with n nodes, if r(n) is the transmission or sensing

radius and Dij(n) is the number of transmissions required to traverse from node i to

node j. The diameter of the grid is then defined as:

D(n) = maxi,jDij(n) (3.10)

From which the authors derive the upper and lower bounds on the diameter of

the grid as:

√2 < r(n)D(n) <

2

1 − 2√πc

(3.11)

Where c is a parameter that decides how much power each node uses.

Two hardware empirical studies were conducted in [101] using Berkeley Mica

motes running TinyOS [49]. The goal was to test the reliability of the wireless commu-

nication link. For the first experiment, a group of sensors were placed linearly with a 2

feet spacing between each pair. One node was chosen as a transmitter sending periodic

65

packets at a given power level. The rest of the nodes were acting as receivers counting

the number of received packets. Figure 3.13 shows the results obtained. As expected,

there is a region within which all nodes have good reception. The size of this region

depends on the transmission power. There is also a distance beyond which all nodes

have poor connectivity. Between the two points there is a transitional region within

which the overall connectivity drops smoothly.

Figure 3.13: Reception probability of all links in a network with a line topology [101].

To test whether the link quality is stable over time, the second experiment was

conducted in an indoor environment using a pair of nodes. One node was configured

as a data source, sending 8 packets per second and the other node configured as a

receiver. The test was carried out for a period of 20 minutes at a distance of 15 feet.

The nodes were then brought to a distance of 8 feet and the experiment was continued

for a total time of 4 hours. Figure 3.14 shows the results of this experiment. At each

distance the mean link quality is relatively stable, but there is significant variation in

the instantaneous link quality. The authors in [101] also state the observation that

66

some distant pairs have better reception than relatively closer ones. The significance

of these experiments is that they highlight the challenges that wireless communication

introduces and its effect of the overall network reliability.

Figure 3.14: Reception probability variation over time across a single link [101].

3.2.5 Cross-Layer Reliability

The argument for assigning different reliability levels based on the message type

was made in [41]. The authors point out the fact that routing techniques so far did

not differentiate between data with high reliability requirements and data with low

reliability requirements. Thus, the network will undergo the same overhead and cost

regardless of the importance of the data. To improve data delivery reliability for

critical data, the proposed approach is to create multiple routes between source and

destination. The number of routes through which the message is sent is a function

of the message reliability requirement level. To improve the data delivery reliability

the authors present the ETX algorithm. This algorithm attempts to find paths with

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the smallest number of expected transmissions (including retransmissions) required to

deliver a packet. It predicts the number of retransmissions required using per-link

measurements of packet loss ratio in both directions of each wireless link. This packet

loss ratio is calculated using the number of probes received in w seconds over the link

and the actual number of probes that should have been received. The protocol has

these characteristics: It is based on delivery ratio, which affect throughput. Can use

precise link loss ratio to make fine-grained decisions between routes. Penalizes routes

with more hops, which tends to have lower throughput due to interference

From the algorithm description, the data delivery reliability is translated to a

throughput and latency problem. The AODV [67], DSR [40] or other Ad-hoc protocols

are given as possible routing strategies to work on top of the ETX algorithm. Drawback:

This solution may be fit for source-to-destination communication style common to ad-

hoc type networks. This may not be the case for the typically hierarchal WSNs.

3.2.5.1 Cross-Layer Reliable Protocol Using Embedded Message Reliabil-

ity Flag

To improve network lifetime, nodes can buffer messages until their buffers are

filled and then send all messages to the upper level nodes. The impact of this in-

network buffering on reliability was studied in [25]. A one-bit reliability flag in the

message is proposed. If the flag is not set, the network will do its best effort to deliver

the message while conserving energy. If the flag is set, the network will transform itself

into a reliable data delivery system using MAC layer ACKs.

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3.3 Wireless Sensor Networks Reliability Techniques

From the surveyed WSNs reliable protocols literature, some of the proposed

techniques for improving WSNs network reliability can be recognized.

• Link layer retransmissions.

• Increasing the one hop transmission power. This leads to longer transmission

range, but very taxing on the power requirements.

• Use of ACKs and NACKs (at the link or transport levels).

• Use of multiple disjoint paths to send the same message and adding redundancy

to each packet (erasure codes).

3.4 Wireless Sensor Networks Reliability Challenges and Open Issues

In this section, we summarize WSNs reliability challenges and point out some

open issues.

3.4.1 Wireless Sensor Networks Reliability Challenges

Some of the surveyed work point out some reliability challenges specific to

WSNs. [80] Discusses the need for incorporating Common Cause Failure (CCF) in

the reliability calculation of WSNs since communication failure can be due to link fail-

ure or node failure, which in turn can be due to CCF. CCF [66] represents node failure

that can be due to events that have a low probability of occurrence, but will disable

a large number of nodes when they do occur. Examples of CCF failure are flooding,

fire, and mud slides.

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As another example of reliability challenges specific to WSNs, [20] points out

the challenges introduced by the silent failure of nodes due to energy depletion.

3.4.2 Wireless Sensor Networks Reliability Open Issues

As summarized in [100], wireless senor networks data delivery reliability has

room for research in the following areas:

• Design and evaluation of new mechanisms for improving reliability taking into

consideration the complex behavior of the wireless channel and the interaction

between the different networking stack layers.

• Ways to adaptively control the mixture of mechanisms used in the network ac-

cording to the current data delivery reliability measurements and the target re-

liability.

• Consideration of timing aspects and the effect of reliability demands on real time

requirements and on the network’s ability to meet deadlines.

Our proposal for this dissertation can be considered as an attempt to address

the wireless sensor networks reliability issues discussed above.

3.5 Summary

This chapter surveyed and classified the research in reliability for wireless sensor

networks. The classification followed the OSI stack model as explained and used in

Chapter 2. In this Chapter, we highlighted that fact that past research in reliability

for wireless sensor networks was in isolation from energy conservation research.

70

Energy-efficient protocols proposed for wireless sensor networks are surveyed

in Chapter 2. In this Chapter, we surveyed the reliable protocols for wireless sensor

networks. Cross-layer energy-efficient protocols and cross-layer reliable protocols pro-

posed in the literature are also covered in the surveys. In the next chapter, we present

our proposed reliable, energy-aware, cross-layer protocol for wireless sensor networks.

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Chapter 4

RELIABLE, ENERGY-AWARE CROSS-LAYER

PROTOCOL FOR WIRELESS SENSOR NETWORKS

In classical systems reliability research, usually the challenge is finding models

and solutions to the randomness of the component failure problem. In wireless com-

munication systems, the inconsistent nature of the wireless communication channel

adds another unique set of communication reliability challenges. In this research, the

main goal is to raise the level of awareness about the need to consider communication

reliability in wireless sensor network research. It needs to be added as a constraint

to the energy optimization and other efforts. To achieve this goal, we design a reli-

able, energy-aware protocol for wireless sensor networks. The protocol benefits from

the greater optimization levels that cross-layer design provides. This is realized while

making sure to eliminate the problems that cross-layer designs are known to introduce.

In this chapter, we present the proposed protocol in details.

4.1 Classifying Wireless Sensor Networks Research

As discussed in the previous chapters, two focus areas in WSNs research can be

identified. One area is concerned with the problem of optimizing WSNs energy perfor-

mance. This area did not consider reliability as a design constraint or as a performance

72

parameter. The second area is focused on WSNs reliability modeling, analysis and reli-

able protocols design. Research in this area has usually been independent of the energy

conservation issues that characterize these networks. In several cases, the reliability

research in WSNs has been independent of the OSI networking stack.

4.1.1 Wireless Sensor Networks Energy Optimization Research

Wireless sensor networks are known to be application specific. The nature

of message exchange between the nodes and the Base Station is mostly of reporting

sensor readings. In many applications, the sensors are battery-powered and usually

placed in remote, hostile or hazardous areas. Manual service of sensor nodes after

their deployment may not be economically feasible. In some situations, manual service

may not be even possible. For this reason, the node’s lifetime is dependent on the

battery’s lifetime. This places greater challenges and constraints on the network design

thereby requiring the optimization of energy consumption. A new set of protocols and

optimizations has been proposed to address the energy and other challenges in wireless

sensor networks. A comprehensive survey and discussion of these energy optimizations

techniques and protocols have been presented in Chapter 2. Cross-layer optimizations

techniques, benefits and drawbacks have also been discussed there.

4.1.2 Wireless Sensor Networks Reliability Research

The research in reliability for wireless sensor networks is relatively new. There

has been no agreement so far on a unified definition for wireless sensors’ reliability. Each

study has defined reliability differently and in line with their approach. In Chapter 3,

we presented a survey of wireless sensor networks reliability research. There, we tried

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as much as possible to classify the reliability protocols and algorithms into groups that

are aligned with the OSI network stack.

4.1.3 Bridging the Gap

In this research, we propose a reliable, cross-layer protocol for WSNs. The pro-

tocol benefits from the body of research in the two areas of wireless sensors reliability

research and wireless sensors energy conservation research. The protocol takes advan-

tage of the energy savings techniques while providing a reliable data delivery network.

Figure 4.1 shows an illustration of this proposal’s approach.

4.2 Proposed Protocol Network Settings

The network settings we consider for this protocol are very large, hierarchal,

monitoring and surveillance networks. The network is assumed to be hybrid in its

message generation, managing a combination of proactive and reactive messages. In

addition, we assume that the network is composed of stationary, homogeneous wireless

sensor nodes and one Base Station. The nodes are battery powered and energy effi-

ciency is a critical requirement for the nodes and for the network operations. Nodes

organize themselves in clusters around an elected cluster head (CH) node similar to

LEACH [35]. The Base Station is located far from the rest of the nodes as shown

in Figure 4.2. The Base Station has no restrictions on its energy resources and is

connected to a stable, continuous power supply.

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Figure 4.1: Bridging the gap, reliable, energy-aware protocol design.

4.3 Proposed Protocol Architecture

In this section, we present the architecture details of our proposed protocol.

First, we cover the messages that the protocol defines. We present the messages format

and the different messages reliability requirements. Second, we introduce our proposed

‘Link Rating’ parameter and explain how it is used by the protocol in optimizing the

one-hop link energy setting and in clusters formation. Next, we introduce the MAC

layer proposed Dynamic Backoff algorithm. Finally, we present the protocol’s start-up

phase and the steady-state operation algorithms.

4.3.1 Protocol Messages

The proposed protocol defines four types of messages: periodic reports, event

notification, infrastructure communication messages, and the proposed Hello and Hello-

reply control messages.

The first type of messages is the periodic reports. These messages are generated

by the nodes and sent to their cluster heads to be aggregated and relayed to the Base

Station. The purpose of these messages is to allow the Base Station to monitor the

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health of the network similar to the periodic reports used by APTEEN protocol [59].

Figure 4.2: Proposed protocol network setting.

The second type of messages is the event notification messages. These are

generated when the sensors detect the presence or the occurrence of the monitored

phenomenon, turn on their radios and send their reports to their respective cluster

heads. The periodic report messages and the event notification messages are generated

by the application layer. They have message format as shown in Figure 4.3.

The third type of messages is the infrastructure communication messages. These

are networking and control messages generated by the network layer. They are ex-

changed between the network layers in the different nodes to manage the network

infrastructure. Examples of these messages are neighbors’ discovery messages, cluster

head advertisement messages inviting other nodes to join, and join messages sent by the

member nodes. The reliable communication of the infrastructure communication mes-

sages is critical to the network infrastructure and to the routing protocol’s correctness

[80].

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4.3.1.1 Proposed Protocol Messages’ Reliability Settings

The proposed protocol defines three levels of reliability. The highest level of

reliability is assigned to event reporting messages. The justification for this assignment

is that event reporting is the reason for establishing the network in the first place.

Periodic reports have the lowest reliability since they are only needed to show that

the network is alive and functioning. Also, if readings from one reporting period are

not received, there is a chance they will be acquired in the next reporting period. The

infrastructure communication messages will have medium reliability setting in between

the other two message types. The Hello and Hello-reply messages do not have reliability

settings attached to them. These messages are considered as mechanisms used by the

proposed protocol to determine links reliabilities.

Figure 4.3: Application and infrastructure communication message format.

4.3.1.2 Periodic Report Messages

As stated, these messages only serve the purpose of updating the Base Station

about the health of the network. This makes periodic reports the least demanding

in terms of their reliability requirements. At each reporting interval, the application

layer will generate a report message and set the message reliability flag to ‘LOW’. The

different reliability levels and the corresponding reliability flag values (RL) are shown

in Table 4.1.

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The protocol’s objective for these messages is to optimize energy consumption

at the expense of the reliable delivery of these messages. A report message is created

by the application layer and passed to the network layer and from there to the MAC

layer. When the message is in the routing layer, the message’s reliability flag will be

used to select the corresponding power setting that will be used by the radio layer in

transmitting the message. When the message is at the MAC layer, the reliability flag

will be used to set the parameters of the proposed Dynamic Backoff algorithm. The

actual values assigned to these parameters will be application specific. The proposed

MAC layer Dynamic Backoff algorithm is presented and explained in Section 4.3.3 of

this chapter.

4.3.1.3 Event Reporting Messages

Event detection and reporting is the main mission of the wireless network. Event

notification messages then need to have the highest reliability settings. When an event

is detected, all nodes within the event detection radius will switch on their radios and

send an event notification message to their cluster heads. Once received by the cluster

head, the event message will be immediately relayed to the Base Station. The format

of this message is similar to the periodic reports as shown in Figure 4.3. The reliability

flag field will be set to ‘HIGH’.

Event-to-Sink vs. Source-to-Sink Reliability: As discussed previously in

Chapter 3, the successful detection of an event depends on the Base Station receiv-

ing a notification about the event. This is independent of the number of nodes that

were successful in delivering their event reporting messages to the Base Station. This

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scenario is known as event-to-sink reliability [28]. It is different from the peer-to-peer

setting where the communication reliability depends on the successful reception of every

message. (The latter scenario is known as source-to-sink reliability.) In the proposed

protocol, the event-to-sink reliability is more applicable for the type of applications

considered for the protocol.

Table 4.1: Reliability level (RL) assignment and value for the three message types.

Message type Reliability requirements RL valueEvent notification HIGH 0Infrastructure messages MEDIUM 1Periodic reports LOW 2

4.3.1.4 Infrastructure Communication Messages

The infrastructure communication messages are control messages originating

from the networking layer. They are needed for setting up and maintaining the network

infrastructure. Examples of these messages are cluster head advertisement messages,

and reply messages from joining nodes. These messages are important for the network’s

infrastructure correctness.

When a node needs to advertise itself as a cluster head or when nodes are

interested in joining a particular cluster head, the network layer will construct and send

out an infrastructure communication message to communicate the interest. The format

of this message is similar to the periodic reports’ format shown in Figure 4.3, with

the reliability flag field set to ‘MEDIUM’. The difference between the infrastructure

communication messages, periodic reports, and event reporting messages is that the

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infrastructure communication messages originate from the network layer while periodic

reports and event reporting messages are created by the application layer.

The CSMA MAC standard [1] defines and uses some control messages e.g. Re-

quest To Send (RTS), Clear To Send (CTS), Acknowledgment (ACK) and Negative

Acknowledgment (NACK). In the proposed protocol, these messages are not considered

as part of the infrastructure communication messages. They are considered as relia-

bility mechanisms used by the MAC layer to ensure upper layers message reliability.

Therefore, the reliable delivery of these MAC control messages is not considered by the

proposed protocol.

4.3.1.5 Proposed Hello and Hello-reply Messages

Once operational, the nodes will start sending periodic Hello messages to their

one-hop neighbors. This exchange will use different transmission power levels. This

Hello exchange is used for two different algorithms and calculations. First, it is used to

set up individualized link power settings. Second, it is used to calculate the proposed

Link Rating parameter for each of the node’s one-hop neighbors. The format of the

Hello message is shown in Figure 4.4.

The Hello messages will not be forwarded once received by a node. This can

be achieved by the node intelligently examining and identifying the message and not

forward it. Alternatively, this can be achieved by setting the time-to-live (tty) flag to

one in the message before transmitting it. The time-to-live (tty) flag is a standard

networking flag that indicates the number of hops that a message is allowed to traverse

before getting discarded.

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The Hello-reply messages are unicasted back by the one-hop neighbors to the

Hello message originator. The format of the Hello-reply messages is shown in Figures

4.5. Each node will use this periodic exchange to build statistics for the per link

reliability-to-power ratio. A flowchart for the Hello messages exchange is shown in

Figure 4.6. This exchange between neighbors and the resulting per link reliability to

power ratios are needed for the proposed ‘Link Rating’parameter algorithms.

4.3.2 Proposed Protocol Routing and Clustering Algorithms

As stated earlier, the proposed protocol is a cross-layer design. It has a network

layer routing component, a MAC layer component and a radio component. The MAC

layer proposal, the Dynamic Backoff algorithm will be presented in the next section.

In this section, we present the proposed routing algorithms.

Figure 4.4: Hello message format.

Figure 4.5: Hello-reply message format.

4.3.2.1 Wireless Communication Challenges and Existing Solutions

The work in [25] points out that the link quality varies over time and from

node to node. This argument justifies using individualized link power settings. Several

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publications have proposed methods for measuring and coping with the variations in

the link quality [95], [47], [25]. These methods were surveyed in Chapter 3.

In [47] the concept of blacklisting was introduced. A link will be classified ‘bad’as

soon as and for as long as its reliability falls below a certain threshold. Nodes maintain

a list of their one-hop links and will relay messages through the ‘good’links only. The

authors in [101] improved on the blacklisting concept and proposed a moving average

link reliability estimator, called Window Mean with Exponentially Weighted Moving

Average (WMEWMA). It is defined as the average success rate over a given link. These

methods attempt to build measures for the one-hop link reliability. Nevertheless, both

methods failed to consider the associated energy consumption. They also rely on the

existence of multiple routes between the source and the destination entities. This

may not be the case in hierarchal architectures typically suggested for wireless sensor

networks.

4.3.2.2 Proposed Individualized Reliable Link Power Settings Using the

Hello Exchange

Energy efficiency is critical to WSNs operations. We aim to rectify the short-

comings of the two methods discussed above by jointly considering energy and link

quality calculations.

The exact values for high, medium, and low reliability requirements are specified

by the application. On each link, nodes will exchange periodic Hello and Hello-reply

messages. The transmission power setting for the Hello messages will be varied in steps.

The initial (maximum) exchange power setting, the power step size, the minimum

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Figure 4.6: Hello messages exchange flowchart.

exchange power setting, and the number of messages exchanged at each setting are

predefined and programmed in the sensors firmware.

Each node maintains a neighborhood table. An example of this table is shown

in Figure 4.8. Details about the different fields in the neighborhood table and how

they are used in our proposal will be covered in Section 4.3.4 and 4.3.5. For each

neighbor, the link reliability statistics for each power setting will be recorded. This

can be explained mathematically by the following formula:

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Measured link reliability =number of Hello − reply messages received

number of Hello messages sent(4.1)

In order to obtain a value for the transmission power that will give ‘high’ relia-

bility on a given link, the node selects the record with the minimum power where the

reliability is higher than or equals to the ‘high’reliability value specified by the appli-

cation. The same procedure will be used to obtain the power setting for ‘medium’and

‘low’reliability on the link.

The method used in [95] assumes a fixed power setting and selects the link

that gives the best reliability. On the other hand, our proposed protocol varies the

transmission power and selects the minimum transmission power that meets or exceeds

the required reliability.

Impact of Hello, Hello-reply Overhead: The Hello, Hello-reply messages

exchange introduces non-negligible energy overhead. The energy savings gained maybe

compromised by the energy overhead introduced. Several optimizations can be applied

to reduce this energy overhead. As an example, the Hello exchange can be limited

to a predefined number of messages. The graph in Figure 4.7 was obtained through

simulation. It shows that the required power setting for each of the three reliability

levels was stable after 20 to 30 Hello messages exchanged. This is a clear indication

that any messages exchanged beyond that will not change the power settings and will

only be a waste of valuable energy resources.

Care must be exercised in setting the number of Hello messages per power

setting per link and the power step size. Otherwise, the energy savings gained will be

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wasted on the overhead introduced by sending these Hello and Hello-reply messages.

The impact of this Hello exchange energy overhead is studied through simulation. This

is presented in details along with results in the performance evaluation chapter.

4.3.2.3 Proposed Link Rating parameter

The Hello messages exchange will be used to obtain link quality statistics, which

in turn will be used for the proposed Link Rating parameter. The Measured link relia-

bility was defined as given in Equation 4.1. The Link Rating is defined as the average

of ratios of the measured link reliabilities for high, medium and low reliabilities to the

power setting needed to achieve those reliabilities. This can be expressed mathemati-

cally as:

LinkRating =

∑ RL

PL

N(4.2)

Where RL is the measured reliability for level L, PL is the power value needed to achieve

level L’s reliability. N is the total number of reliability levels defined.

The Link Rating parameter, as defined in Equation 4.2, can be used for any

number of reliability levels (N). Since our protocol defines three levels of reliability

settings, low, medium, and high, the value of N in our proposed protocol will be three.

Also, since the Hello messages are periodic, this will result in the Link Rating parameter

almost always reflecting the current link quality. The Link Rating parameter will be

used for cluster formation as explained in the next section.

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4.3.2.4 Clusters Formation Using the Proposed Link Rating Parameter

Similar to most WSNs protocols, the proposed protocol is hierarchal. Nodes

organize themselves in clusters around an elected cluster head (CH) node. Several

WSNs protocols use this hierarchal organization as an energy conservation technique

[35], [52], [58], [59].

The Link Rating parameter was introduced and explained in the previous sec-

tion. It is a local algorithm that every node executes for each of its one-hop neighbors.

The nodes calculate and store a Link Rating value for each of their one-hop neighbors.

At the end of each cluster cycle (or round), new set of nodes elect themselves as clus-

ter heads similar to LEACH election mechanism. The new cluster heads will generate

cluster head advertisement messages that are broadcasted to nodes within their cov-

erage region. When a non-cluster head node receives the cluster head advertisement

messages, the node will join the cluster head with the best Link Rating parameter

value resulting in the optimum reliability to link power setting.

Figure 4.7: Minimum number of Hello messages exchanged.

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4.3.2.5 Proposed Protocol Energy Optimization Under Reliability Con-

straints

One of the objectives of this work is to couple reliable communication with en-

ergy optimization in wireless sensor networks. The proposed protocol uses the Link

Rating parameter in its cluster head selection algorithm. The individualized link power

settings algorithm was introduced in Section 4.3.2.2. The protocol uses the individual-

ized link power settings from the neighborhood table for the communication between

the nodes and their respective cluster heads. The use of these two techniques is where

our proposed protocol couples the energy conservation requirements with satisfying

reliability constraints.

4.3.3 Proposed MAC Dynamic Back-off Algorithm

The Dynamic Backoff algorithm is the second component of the proposed cross-

layer protocol. The protocol runs on top of CSMA-type MAC layer protocols. Exam-

ples of these protocols are IEEE 802.11 [1] and S-MAC [102].

The message reliability flag will be used to set the MAC back-off timer dynam-

ically using the equation below. The reliability level (RL) values will be set as given in

Table 4.1.

Backofftimer = C ∗ Random(x) + K ∗ (RL) (4.3)

Where Random(x) generate a random number in the range 0 to x, RL is the reliability

level required for the current message, k and C are constants.

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Figure 4.8: Example of neighborhood table with data.

It is important to note that the optimized values for the constants x, C and K

are dependent, among other parameters, on the average message length. Also, under

heavily utilized or congested network, the effectiveness of this mechanism is sensitive

to the MAC retry limit. From the above formula and the reliability level RL value for

periodic reports, we conclude that they will have the longest MAC contention back-off

timer.

The reliability flag will also be used to set the number of retransmission at-

tempts. The exact number of retransmission will depend on the application. Under

light or moderate channel loads, the effect of the MAC layer algorithms will be shorter

delays for the more important messages. Under congested or heavy bandwidth utiliza-

tion, the MAC algorithms will drop the less important messages and free the channel

to manage the delivery of the important messages.

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The combination of maximum retransmission attempts and short back-off peri-

ods between successive attempts are meant to give event-reporting messages optimal

chance of being received by the Base Station. The impact of these algorithms is studied

through simulation analysis. As results presented in the next chapter will show, the

proposed protocol outperforms APTEEN in the successful delivery of event messages.

4.3.4 Proposed Protocol Startup Phase

When the network has just been deployed or when fresh nodes have been added

to an existing network, there is not enough data about the one-hop links quality for the

protocol to use. In this case, the nodes will start monitoring the radio channel and will

join the cluster head with the strongest signal power. This is similar to basic LEACH

protocol setting. Nodes will communicate with their respective cluster heads using the

minimum power that guarantees network connectivity. The same power setting will be

used for all intra-cluster communication, regardless of the message type. Again, this

is similar to LEACH protocol setting.

The nodes have already been programmed with the networking and other pa-

rameters that they will use. These parameters include the hard and soft threshold

values for the environmental variable that the network is monitoring and the amount

of time between two successive periodic reports similar to APTTEN protocol. The

nodes’ firmware is assumed to be preprogrammed with the frequency of sending the

one hop Hello messages, the number of message types and the reliability required for

each of these types. The nodes have also been programmed with the minimum number

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of Hello messages needed per link per power level. This is the number of Hello mes-

sages needed before statistics collected about the reliability of the level can be used

by the protocol. While the link statistics are being collected, the protocol will behave

similar to APTEEN. This setting will persist until enough link statistical information

becomes available for the proposed cluster head selection and the individualized link

power setting algorithms to be used. There are many ways to develop algorithms to

ensure that the statistics collected are not used until the minimum exchange conditions

are met. Here, we explain the method we chose to implement.

As illustrated in Figure 4.8, a Boolean field is part of each node’s power level

record, called ‘bCanBeUsedForLinkRating’. The field initial value is FALSE. When the

predefined minimum number of Hello messages exchanged is reached, the value will be

set to TRUE and will not be modified again. Similarly, there is a Boolean field in each

node’s record. The initial value is also FALSE. This value will be set to TRUE when all

the power level records are TRUE. Finally, a Boolean value for the whole neighborhood

table is initially FALSE. The value will be set to TRUE when all the links records are

TRUE. The proposed protocol’s cluster head selection and individualized link power

setting algorithms will be used only when the table’s Boolean variable is TRUE. In the

performance evaluation, we examine the effect of varying the number of Hello messages

per power level on the protocol’s performance.

4.3.5 Protocol Steady-State Operation

When the network has been operational for some time, enough link statistics

will become available. Neighbors’ information stored can be used as indicated by a

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TRUE value in the neighborhood table’s Boolean variable. Once the next cluster head

selection round starts, cluster head selection will be based on Link Rating performance.

The steady state operations are illustrated by the flow chart in Figure 4.9. The com-

munication with the cluster head will use the link power values corresponding to the

message’s reliability requirements. These values are available from the record associ-

ated with the cluster head in the node’s neighborhood table. The sooner these settings

can be used, the better the energy performance that can be obtained. On the other

hand, if the values are used too soon, they may not be the best optimal values. In the

next chapter, we present an investigation into these performance tradeoffs.

4.4 Summary of Contributions

The contributions of this work can be summarized in the following:

• A reliable, energy-aware cross-layer protocol that benefits from the body of re-

search in the wireless sensor networks reliability and wireless sensor networks

energy conservation areas. The protocol optimizes energy consumption while

providing a reliable data delivery network.

• The proposed protocol classifies the network messages based on their type. We

outline a cross layer setting in which each message carries its own reliability

requirements. In the selected network setting, three message types were proposed:

event reporting, periodic reports, and infrastructure communication messages. A

different level of importance and reliability requirements is then attached to each

message type.

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• The proposed Link Rating is used by the protocol’s networking layer to opti-

mize the cluster formation, while observing reliability constraints. Nodes will

communicate with their cluster heads using the Individualized Link Power Set-

ting algorithm. This will optimize intra-cluster communication under reliability

constraint.

The proposed Link Rating parameter defined by the formula:

LinkRating =

∑ RLPL

N

Where RL is the reliability measure for level L, PL is the power required to achieve

level L’s reliability. And N is the total number of reliability levels.

• At the MAC layer, we propose the Dynamic Back-off algorithm. It is a random-

ized back-off algorithm that is applied to adjust the back-off timer based on the

message reliability setting. The result is that in congested or heavily utilized

bandwidth, messages with high reliability requirements will have shorter back-off

times, thus increasing their chance of getting delivered. Another part of the Dy-

namic Backoff algorithm is varying the number of MAC retransmission attempts.

The number of retransmissions depends on the message type. Messages with high

reliability requirements have a higher number of retransmission attempts.

4.5 Summary of Proposed Protocol’s Cross-layer Techniques

The techniques used by our proposed cross-layer protocol at each layer are out-

lined below and summarized in Figure 4.10.

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Figure 4.9: Proposed Reliable Protocol steady-state operation flowchart.

• First, the application layer will attach a reliability level to each message it sends.

This depends on whether the message is a periodic report or an event notification

message. Similarly, the network layer will assign a reliability level to the infras-

tructure control messages it sends. These flags will be used by the other layers

to set their parameters accordingly.

• The routing layer will read the message’s reliability flag, read the corresponding

transmission power for the neighborhood table and set the radio transmission

power level.

• The MAC layer has multiple roles in this protocol. It reads the message’s relia-

bility flag that was set by the upper layers and dynamically adjusts its back-off

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Figure 4.10: Proposed protocol Cross-layer techniques.

timer. Messages with high reliability requirements have a shorter back-off, hence,

a better chance of getting through. This is also coupled with higher retransmis-

sion limits for messages with higher reliability requirements.

• The radio will use the transmission power that was set by the routing layer.

4.6 Related Protocols

In this section, we present a comparison between the proposed protocol and the

other similar protocols for wireless sensor networks.

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4.6.1 LEACH

LEACH [35] is a hierarchical protocol proposed for WSNs with large number

of homogeneous, resource-constrained nodes monitoring the environment. Nodes are

periodically sending their readings to a base station located far away from the field.

The protocol achieves its power saving goals by allowing a small percentage of the

nodes, called cluster heads, to collect data from their surrounding neighbors, aggregate

that data and send a report to the Base Station representing the combined readings.

The protocol avoids depleting the cluster heads energies by selecting a new set of cluster

heads at the beginning of each round. The protocol uses a randomized routine for each

node to elect itself as a cluster head. This routine is run locally by each node, to avoid

the traffic overhead of a centralized routine. Simulation results show that LEACH

can increase the network lifetime by as much as a factor of eight compared to direct

transmission.

Our proposed protocol is similar to LEACH in being hierarchical and has pe-

riodic reports sent to the Base Station. But unlike LEACH, our proposed protocol

observes reliability constraints, cross-layer design and in addition to the periodic re-

ports, sends event notification messages.

4.6.2 TEEN/APTEEN

Similar to LEACH, TEEN [58] is also a hierarchical protocol. The protocol

defines and uses two parameters, a hard threshold and a soft threshold values. The

sensors are assumed to monitor the environment continuously. If the value of the sensed

parameter reached or exceeded the hard threshold value, the nodes will turn on their

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transmitters and send event notification reports to their cluster heads. To prevent the

nodes from flooding the network with reports once the hard threshold is reached, the

nodes will send a new report only if the value of the sensed parameter exceeds the last

reported value by an amount equals to at least the soft threshold value. As already

stated in [58], the main drawback of this protocol is that if the threshold value is never

reached, the user will get no reports at all and will not be aware if all the nodes in the

network are dead. This limitation of the TEEN protocol was removed by introducing

a hybrid version of the protocol, the APTEEN protocol [59]. APTEEN defines a new

Count Time (CT ) parameter that is also under the user’s control. The count time is

defined as the maximum time between two successive reports.

Our proposed protocol shares the event-driven reporting mechanism introduced

in TEEN and APTEEN. It has the same hybrid reporting feature that APTEEN

uses. The differences between our proposed protocol and TEEN/APTEEN are that

our protocol takes advantage of cross-layer energy optimization techniques. It also

considers reliability as a design constraint.

4.6.3 ESRT Protocol

Event-To-Sink Transport (ESRT) is a reliable transport protocol proposed for

wireless sensor networks. It is a centralized protocol that runs only on the sink and thus

leveraging its abundance of computing and power resources and relieving the resource

constrained nodes. In ESRT, congestion control is identified as an important factor

for reliable data flow since packet loss due to congestion can impair event detection at

the sink. The reliability requirements are determined by the application layer. The

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transport layer reliability is defined by a reliability factor that is the ratio between the

number of received packets at the sink to the optimal number of packets that is required

for reliable event detection. Ideally, the ratio should be maintained as close as possible

to one. The reporting frequency is defined as the number of reports that the nodes

need to generate per unit time to achieve required event detection reliability. This

factor is calculated and broadcasted to all nodes by the sink. The protocol operation

also relies on congestion detection. To do this the sink relies on the nodes setting a

congestion flag bit on their reply messages. A node will monitor its buffer fullness and

the rate at which the buffer is getting filled. The congestion flag bit will be set if the

node predicts that it will experience a buffer overflow during the next reporting period.

The proposed protocol is similar to ESRT in considering the reliability con-

straints. The differences between the proposed protocol and ESRT are that the pro-

posed protocol is cross-layer, uses energy optimization techniques and scalable.

4.6.4 ETX, Erasures Codes Protocols

The ETX algorithm attempts to find paths with the smallest number of expected

transmissions required to deliver a packet. It predicts the number of retransmissions

required using per-link measurements of packet loss ratio in both directions of each

wireless link. This packet loss ratio is calculated using the number of probes received

in w seconds over the link and the actual number of probes that should have been

received. The AODV, DSR or other Ad-hoc protocols are given as possible routing

strategies to work on top of the ETX algorithm.

The use of erasure codes improves reliability by sending the data message

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through multiple paths. The algorithm tries to find k disjoint paths between source

and destination and adding redundancy to each packet so that only E (E < k) packets

need to reach the destination for the original message to be constructed.

The proposed protocol shares similarities with the ETX and Erasure codes pro-

tocols. They all observe reliability as a performance constraint. The differences be-

tween the proposed protocol, ETX and Erasure codes are that the proposed protocol

is cross-layer, uses energy optimization techniques, scalable and hierarchical.

4.6.5 Cross-layer Protocols



mance. It has been used in the ad hoc wireless systems to improve throughput, latency,

and quality of service (QoS). Due to the severe energy constraints that are common

to wireless sensor networks operations, several publications have proposed cross-layer

design as an optimization technique [87], [73], [50], [68], [57], [96], [98], [53], [28], [104],

[82].

4.6.5.1 Benefits of Cross-layer Designs

As previously discussed in Chapter 2, Examples of the benefits of cross-layer

designs for wireless sensor networks are:

• The stringent energy, storage and processing capabilities of the sensor nodes

necessitates such approach

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• There is a significant overhead associated with the layered protocols resulting in

high inefficiency

• Some recent empirical studies necessitate that the properties of low power ra-

dio transceivers, which is common to wireless sensors, and the wireless channel

characteristics be considered in protocol design

• The event-centric nature of wireless sensor networks requires application-aware

communication protocols.

4.6.5.2 Drawbacks of Cross-layer Designs

The cross-layer design performance improvements gained come at a price. This

includes decreased architecture modularity, and loss of the decoupling between design

and development. Cross-layer designs may also be hard to debug, maintain or upgrade.

The interdependencies introduced need to be carefully considered and evaluated to

avoid the non-trivial problem of system’s instability.

4.6.5.3 Avoiding Cross-layer Drawbacks in Proposed Protocol

Our proposed protocol uses cross-layer design as a performance and energy

optimization technique. The protocol avoids introducing layer interdependencies by

preserving the stack architecture and optimizes the overall system energy performance

by information sharing. The information is embedded as flags in the data and control

messages that are moving through the stack. Each layer reads these flags and adjusts

its performance and handling of the message accordingly.

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The similarities and differences between our proposed protocol and the other

exiting protocols are summarized in Table 4.2.

Table 4.2: Comparison between proposed protocol and other well-known WSNs pro-tocols.

Protocol Reliable Scalable Cross- Hierar- Event- Periodiclayer chal driven reports

LEACH [35] X X No XTEEN [58] X X Source-to-sink

APTEEN [59] X X Source-to-sink XESRT [75] X Event-to-sink

ETX,[35] X NoErasure Codes[12]Proposed X X X X Event-to-sink Xprotocol

4.7 Summary

This chapter presented the network architecture and cross-layer algorithms of

the proposed protocol. The proposal suggests classifying messages based on their relia-

bility demands. The protocol introduces reliability as a constraint in reaching minimum

communication power levels. The proposed Link Rating parameter is presented in this

chapter. It is defined as the sum of ratios of link reliabilities to the power needed

to achieve those reliabilities. Nodes store a Link Rating value for each of their one-

hop neighbors and will join the cluster head nodes with the best Link Rating values.

The result is reliable communication with the least power expenditure. In addition,

the communication power level used is dependent on the message’s reliability require-

ments. The emphasis here is on using the least radio power that meets or exceeds the

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reliability requirements. The proposed protocol forgoes achieving the highest reliability

levels attainable, in exchange for saving valuable energy resources.

The protocol also introduces a MAC layer component, the Dynamic Backoff

algorithm. The techniques introduced at the MAC layer are meant to boost the delivery

chance of the more important messages at the expense of the less important ones.

Minimum Communication Power: In Hierarchical wireless sensor networks protocols

design, it has been common to assume that nodes will communicate using minimum

power. This assumption has not been studied or qualified in the literature. This work

identified reliable communication as a constraint for this minimum communication

power assumption. The algorithms proposed in this work can be used as mechanisms

to find solutions that satisfy these conditions of minimum communication power.

The performance of this proposal is validated through simulation. Results and

analysis of the simulation experiments are presented in the next chapter.

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Chapter 5

RELIABLE, ENERGY-AWARE PROTOCOL

PERFORMANCE EVALUATION

In this research, the objective is to design a reliable, energy-aware protocol for

wireless sensor networks. The protocol benefits from the greater optimization levels

that cross-layer design provides. This is done while making sure to eliminate the

problems that cross-layer designs are known to introduce. In the previous chapter,

the proposed protocol was presented in detail. This chapter covers the simulation

experiments and performance evaluation of the proposed protocol.

5.1 Proposed Protocol Performance Evaluation Method

Performance measurements in WSNs can be carried out using mathematical

modeling or simulation [80], [56].

WSN performance analysis is complex due to a number of factors. These include

the sheer number of nodes in the system, dynamic topology, network stack layers inter-

action, and power considerations and constraints. Simplifications to the mathematical

model are unavoidable otherwise it will be unsolvable. This in turn will affect the cor-

rectness of the results obtained. Simulation modeling, on the other hand, allows for the

complex networking details and interaction to be captured. In this view, a simulation

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model can be as detailed and descriptive as we need it to be. This will be reflected on

the value and exactness of the results obtained. Due to the factors outlined, simulation

analysis is selected for the proposed protocol’s performance evaluation.

5.2 Performance Evaluation Tools

For the performance evaluation of the proposed protocol, simulation modeling

is used. Simulation is utilized to measure the impact of the proposed protocol and the

performance improvements achieved. In this section, we present our investigation into

finding a suitable simulation tool.

5.2.1 Tool Selection Criteria

Wireless sensor networks are very large networks. The number of deployed sen-

sors in a single network can range from hundreds of thousands to few millions. These

sensors are battery powered, often deployed in harsh and difficult to reach locations.

Serviceability of the nodes is impossible or at least not cost-effective. This discus-

sion shows that scalability and energy calculation are the main factors to use when

evaluating a simulation tool for wireless sensor networks.

5.2.2 Simulation Tools Survey

Several candidate tools were surveyed and evaluated for use in our proposed

protocol’s performance evaluation. Below is a summary description of the surveyed

tools.

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5.2.2.1 Opnet

Opnet [16] is a commercial networking simulation tool. It is used by private

users, network designers and architects to predict the feasibility of any deployable

network. The tool is built on the underlying process model that works on finite-state

machine architecture. Opnet provides a large library of components such as routers

and processing stack protocols. The tool is free for academic users. However, it offers

only limited support for expressing and integrating novel ideas. Opnet scales only to

a few hundred nodes, and has no support for energy calculation. The tool is not open

source and design modifications to the stack layer architecture are not possible.

5.2.2.2 GloMoSim

GloMoSim [63] is written in Parsec (a C language extension) for parallel simu-

lation of wireless networks. It is designed using the parallel discrete-event simulation

capability provided by Parsec language. GloMoSim supports protocols for pure wire-

less networks. Communication protocol stack is divided into a set of layers, each with

its own application-programming interface (API). Models of protocols at one layer in-

teract with the other layers through these APIs. However, the tool is still currently

under development. It is expected to include hierarchal routing protocols to support

scalable network routing in the future. Its developers anticipate adding functionality to

simulate a wired as well as a hybrid network with both wired and wireless capabilities.

The tool scales to a few hundred nodes.

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5.2.2.3 NS-2

NS-2 [61] is a discrete event simulator targeted at networking research. This is

one of the first simulation packages developed as educational and research software. It

is designed to study the network dynamics of TCP/IP network systems. NS-2 provides

substantial support for simulation of TCP, routing, and multicast protocols over wired

and wireless (local and satellite) networks. Many versions of NS-2 are now available.

It is currently being extended to parallel implementation to achieve scalability. Similar

to GloMoSim, the network order for NS-2 is in the hundreds of nodes.

5.2.2.4 PDNS

Parallel/Distributed NS [56]. This is a collection of Extensions and enhance-

ments to the NS-2 simulator. It allows a network simulation to be run in a parallel

and distributed fashion on a network of workstations. To achieve scalability, PDNS

has high hardware demands. The current version of PDNS has been tested on as many

as 136 processors simulating a 600,000+ nodes network topology.

5.2.2.5 JiST/SWANS

The JiST/SWANS [9], [10] is a highly scalable wireless network simulator with

reported results for a million node network. JiST [9] is a high-performance discrete

event simulation engine that runs over a standard Java virtual machine. It is a pro-

totype of a new general-purpose approach to building discrete event simulators, called

virtual machine-based simulation. The simulation platform is very memory efficient.

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SWANS (Scalable Wireless Ad hoc Network Simulator) [10] is built on top of JiST

platform.

Table 5.1 is comparison and summary of the surveyed tools (excluding the

JiST/SWANS). Table 5.2 compares some JiST/SWANS time and memory space re-

sults running simulations of a beaconing node discovery protocol [9] against NS-2 and

GloMoSim simulators.

Table 5.1: Summary of surveyed tools (excluding JiST/SWANS) [105].

Attribute OPnet NS-2 Pdns GLoMoSimCommercially Network systems Network systems Library-based

Focus deployed user- under research under research parallel simulator

network systems study study for Ad-hoc NW

Object orientation C/C++ C++ C++ Extension to C

Memory (Unknown) Dynamic Dynamic Tightly coupled,

management allocation allocation shared & distributed

Simulation Packet-level Packet-level Packet-level Packet-level

resolution

Scale of Operation Few Hundred Few Hundred Few Thousand Few Thousand

(number of nodes)

Intended audience Private users, Network research, Network research Network research

Network designers protocol designers

5.2.3 JiST/SWANS Wireless Ad-hoc Network Simulator

The tool selected for our work is the JiST/SWANS wireless network simulator

[9],[10] because of its scalability. JiST (Java in Simulation Time) [9] is comprised of

four components: A compiler, a byte code rewriter, a simulation kernel and a vir-

tual machine. The simulation programs are written in plain Java. SWANS (Scalable

Wireless Ad hoc Network Simulator) [10] is built on top of the JiST platform. The

SWANS software is designed as separate independent software modules, which could be

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combined to form one wireless network. The SWANS architecture is shown in Figure

5.1. Every SWANS component is encapsulated as a JiST entity, i.e. it stores its own

local state and interacts with other components via interfaces. Each SWANS wireless

device (node) is an entity. The entities within the node are the OSI stack application,

transport, network, routing, MAC, and physical layers. There are also mobility and

routing entities within the node component.

Table 5.2: Comparison between JiST/SWANS, GloMoSim and NS-2 memory andexecution time performance. Reproduced from [10].

Number Performance JiST/SWANS GLoMoSim NS-2of nodes parameter

Execution time 43 s 82 s 7136 s500Memory 1,101 KB 5,759 KB 5,8761 KB

Execution time 430 s 6191 s –5,000Memory 5,284 KB 27,570 KB –

Execution time 4377 s – –50,000Memory 49,262 KB – –

Execution time – – –1000,000Memory 933 MB – –

Figure 5.1: SWANS system architecture with energy model added. Reproduced from[10].

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5.2.3.1 Upgrading JiST/SWANS to a Wireless Sensor Networks Simulator

The JiST/SWANS is a highly scalable ad-hoc wireless network simulator with

reported results for a million nodes network. However, the tool lacked many com-

ponents needed for wireless sensor networks simulation. In previous work, an energy

model and the S-MAC protocol for JiST/SWANS has been developed [89],[90]. Also,

the radio component has been upgraded to include several radio signal fading and path

loss models. This is discussed further in Section 5.4.5. The network layer has also been

upgraded with the ability to select between the default, no packet drop queue, and

a new queue model that drops packets after a certain number of packets are queued

(buffered). The number of packets the network layer can hold is under the user’s con-

trol. A predefined default setting will be activated, if the user does not set the node’s

buffer queue size.

Event creation and event detection is important to simulating wireless sensor

networks monitoring the physical environment. The ability to simulate physical event

taking place in the environment, and the nodes being equipped to sense the occurrence

of these event was not available. To solve this tool limitation, an architectural mod-

ification was implemented to add these capabilities to the JiST/SWANS. Figure 5.2

shows the new sensor, event and radio fading components that were added to transform

the tool to a wireless sensor network simulator.

The JiST/SWANS had a MAC layer implementation of the CSAM protocol

IEEE 802.11 [1] that was incomplete. A working CSMA protocol is needed for the im-

plementation and performance evaluation of the proposed MAC Dynamic Backoff algo-

rithms. Through several efforts, the JiST/SWANS IEEE 802.11 MAC implementation

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Figure 5.2: JiST/WANS additional components for WSNs simulation.

was fixed and upgraded to be used in the proposed protocol’s simulation experiments.

To obtain top-level simulation results, the tool’s architecture has also been up-

dated with a global statistical component that monitors the simulation parameters. Ex-

amples of these parameters are: Total system energy consumed; Average node residual

energy; Average message queues size; Number of messages sent classified by messages

type; Number of messages dropped by the network layers, classified by messages type;

Number of messages dropped by MAC layers, classified by messages type; Number of

messages received at destinations, classified by messages type.

5.2.3.2 Validating the JiST/SWANS for WSNs Simulation

To validate the upgraded simulator, we implemented LEACH [35] protocol on

JiST/SWANS as a representative of WSNs routing protocols. This LEACH imple-

mentation is also used as a foundation for implementing APTEEN, which is used as

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the reference protocol in the performance evaluation. LEACH energy performance re-

sults obtained using JiST/SWANs were in close agreement with the protocol published

Matlab results.

5.3 Proposed Protocol Evaluation Scenarios

In this section, we present the simulation scenarios used to validate, and to

evaluate the performance of the proposed protocol. The reference protocol used in

the evaluation is APTEEN. Each message the proposed protocol generates will have

a field indicating its reliability level. This field will be set by the application layer

for messages created by the application, or by the network layer, for network control

messages originating from the network layer. Details of the proposed protocol messages

were given in Chapter 4, Section 4.3.1.

At every node in the network, the application layer will be sending periodic

reports. From the discussion in the previous chapter, these reports will have the least

reliability demands. Their reliability flag will indicate low reliability setting. Events

are assigned an event radius as discussed in [71]. Events are generated with some

predefined probability at random geographical locations. All nodes with coordinates

within the event radius, upon detecting the event, will send an event message reporting

the occurrence of the event. This type of messages has high reliability requirements.

The routing control messages, generated by the network layer, will have a medium

reliability setting. These messages are cluster head advertisement messages and the

join messages sent by new cluster members.

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5.3.1 Routing and Cluster Head Selection Evaluation Scenarios

In the initial cluster head selection phase, and since the nodes have no informa-

tion about the link qualities for their one hop neighbors, the cluster head selection will

follow the random election algorithm similar to the one used by LEACH and APTEEN

protocols. Once the network is operational, nodes will start building statistics about

their one hop links using periodic ‘Hello’ messages and monitoring the signal levels.

This will be used for the calculation of link ratings that will influence the joining of

future cluster heads. A node will elect to join the cluster head that has the best av-

erage of link rating ratios. The link statistics will also be used to set the transmission

powers used to communicate with the cluster head. A member node will have three

transmission power settings, one for each of the message types.

For the routing layer, a comparison will be conducted between cluster-head

selection (CH) based on our proposed link rating scheme against the random APTEEN

selection criteria. This comparison will also include individualized link power setting

against APTEEN unified minimum intra-cluster transmission power. The performance

parameters measured here will be power consumption and reliability.

5.3.2 MAC layer Evaluation Scenarios

The performance of our protocol at the MAC layer will be tested against APTEEN

that uses SMAC protocol. In this setting, the APTEEN MAC layer has a pre-set re-

transmission limit that applies to all messages regardless of their type. The back-off

timer is also fixed and independent of the message type. For the proposed protocol,

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the MAC layer will have different retransmission limits based on the message reliabil-

ity settings. Messages with high reliability requirements will have higher retry limits.

To be able to test the effectiveness of our proposed MAC layer algorithm, the data-

reporting rate will have to be set to a level that creates a heavily utilized or congested

network conditions. A congested network can be detected when intermediate nodes

start dropping packets because of full buffers.

The first set of experiments will test the effect of setting the retransmit limits

to values according to the following condition:

i < j < k (5.1)

Where i is report messages retry limit, j is control messages retry limit, and k is event

messages retry limit. The parameters i, j and k are integers.

The retransmit limit for the reference scenario will be set to be equivalent to

medium reliability messages. This way, report messages are given below average trans-

mission buffer lifetime and event messages are given better than average lifetime. The

performance parameters are energy consumption, average message latency for each of

the message types, and the reliability of data delivery for each of the message types.

For the periodic reports and the network control messages, the reliability of the mes-

sages will be calculated as the ratio of the number of messages sent to the number of

messages received. For event detection messages, the reliability will be calculated as

the ratio of the number of events detected by nodes to the number of events received

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by the Base Station. This set of experiments will be repeated to test the above perfor-

mance in heavily congested network settings. The goal here is to obtain performance

results for the reliability of event reporting under unfavorable network conditions.

In the second set of experiments, the performance of varying the back-off timers

on reliability will be tested. This will be tested in isolation from the effect of the other

proposed elements. Here again the reference test case is a MAC protocol with a single

back-off timer that has a random duration equivalent to messages of medium reliability

requirements. The dynamic back-off timer has two components as shown below:

Backofftimer = C ∗ Random(x) + K ∗ (RL) (5.2)

Where Random(x) generate a random number in the range 0 to x, RL is the reliability

level required for the current message, k and C are constants.

The performance parameters for these experiments will be average message delay

for each of the three message types, power consumption, and data delivery reliability

for each of the message types.

5.3.3 Radio and Physical Layers Evaluation Scenarios

The radio and physical layer are not evaluated separately. Their evaluation

is part of the routing layer evaluation scenario. The radio for the reference scenario

will have constant radio power setting. This will be compared against the proposed

protocol radio component that sets the transmission power according to the message’s

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power setting specified by the routing layer. The evaluation parameters used are power

consumption and event reporting reliability.

5.3.4 Optimizing the Hello Messages Exchange

As previously stated in Chapter 4, the Hello messages exchange introduces a

non-negligible energy overhead. This overhead may reduce or cancel the energy savings

gained by the Link Rating and the Individualized Link Power Setting algorithms. Below

are the techniques added to the Hello messages exchange algorithm to minimize the

impact of the energy overhead:

Limiting the Number of Hello Messages: Through exhaustive and ex-

tensive simulation experiments, graphs similar to the one shown in Figure 5.3 were

obtained. The graph shows the minimum link power that will result in a commu-

nication that meets the required reliabilities. These values for the reliabilities were

set to 90% for event reporting, 80% for control messages and 70% for periodic report

messages. The graph shows that the power percentages for the three message types

were stable after 20 rounds. In the worst case, the power was stable after 30 rounds.

Any Hello exchange beyond that will not change the power setting values and will only

waste energy and bandwidth resources. Limiting the Hello exchange to a number in

the 20 to 30 messages range will save energy while fulfilling its mission.

Multicasting Instead of Unicasting the Hello Messages: Considerable

energy savings can also be obtained by limiting the number of Hello messages nodes

send in each round. This optimization is achieved by multicasting the Hello and the

Hello-reply messages. Each node will send a single Hello message that contains a list of

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intended recipients. Upon receiving a Hello message, a node will add the sender to its

reply list. After receiving Hello messages from its neighbors, a node will send a single

reply message containing a list of the intended recipients and the relevant information

for each recipient.

Dynamic Minimum Exchange Power Settings: The Hello messages will be

exchanged at different power levels. At the beginning, the messages will be sent at the

initial maximum power setting. After the predefined number of messages for the power

level are sent, the exchange power will be reduced by an amount equals to a predefined

step size. At each level, once the targeted number of messages is reached, the power

will be reduced, until the minimum power level is reached. In some situations, the link

connectivity can be superior. The minimum exchange power can give high reliability

levels. This may be due to the nodes close proximity to its cluster head. Such situation

provides a chance for extra energy savings. The minimum setting can be readjusted. If

for a given link the report messages power setting happens to be equal to the minimum

power level setting, This raises the possibility that a power level below the minimum

level may also produce a satisfactory performance. A new lower minimum power level

setting is selected from a predefined minimum power levels list. This list is stored as

part of the node’s firmware. A new Hello messages exchange will be triggered to test

the reliability performance of the power levels all the way down to the new minimum.

It is important to point out that this new Hello exchange will be triggered only if

the report messages power setting happens to be equal to the minimum setting. This

condition is required in order to avoid unnecessary and meaningless Hello exchange.

Several minimum power settings can be defined up to an absolute minimum. Figure

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5.3.4 shows an example of such settings. Figure 5.5 is a flowchart for the optimized

Hello messages exchange algorithm. It summarizes the optimizations added to the

Hello messages exchange algorithms. These algorithms were discussed in Chapter 4,

Section 4.3.2.2.

Figure 5.3: Optimized Hello messages exchange.

Transmit power levels setting Percentage of inter-clustertransmit power

maximum power level setting 100%1st minimum power level setting 65%2nd minimum power level setting 45%3rd minimum power level setting 20%... ...absolute minimum power level setting 5%

Figure 5.4: Hello messages optimization, Variable minimum link power setting.

5.4 Radio Model for Simulation

Wireless communication link quality is dependent on the radio signal propa-

gation. Link reliability is a measure of the link quality. Modeling the wireless radio

channel is a challenging task [85]. Several factors affect the link quality, some of which

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Figure 5.5: Optimized Hello messages exchange.

are time-varying [101], [49]. No deterministic models exist for the wireless channel. Ex-

isting models either approximate or ignore the channel’s probabilistic nature. Below

are examples of some well-known radio channel models.

5.4.1 Disk Radio Model

This is the simplest signal propagation model. It assumes perfect reception

within a certain distance, and zero reception beyond that. It is a workable solution

when the simulation model needs to be kept simple, and the wireless channel behavior

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has negligible effect on the experiment’s outcome.

5.4.2 Rayleigh fading Radio Model

Rayleigh is a reasonable model when there are many objects in the environment

that scatter the radio signal before it arrives at the receiver. Rayleigh fading is most

applicable when there is no dominant propagation along a line of sight between the

transmitter and receiver. Rayleigh fading models assume that the magnitude of a signal

that has passed through such a channel will vary randomly according to a Rayleigh

distribution. Several mathematical models exist for generating this distribution.

5.4.3 Rician fading Radio Model

Rician fading is a stochastic model for radio propagation caused by partial

cancellation of a radio signal by itself. The signal arrives at the receiver through two

or more different paths. Rician fading occurs when one of the paths, typically a line of

sight signal, is much stronger than the others. The amplitude gain is characterized by

a Rician distribution.

5.4.4 First Order Radio Model

In the first order radio model, the signal strength is assumed to attenuate pro-

portional to some power of the distance traveled. The relationship between the trans-

mitted power and the received power can be expressed mathematically by the following

equation:

PTx = PRx + λ ∗ dn (5.3)

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Where PTx is the transmission power, PRx is the received power, λ is the power-distance

propagation coefficient, d is the distance between the transmitter and the receiver, and

n is a power factor that depends on several environmental factors.

5.4.5 Radio Model Used for Performance Analysis

Several WSNs protocol studies have suggested and used the first order radio

model [35], [58], [59]. Our performance analysis will be carried out against one of these

protocols, APTEEN [59]. For these reasons, the first order radio is the model selected

and implemented in JiST/SWANS for the proposed protocol performance evaluation.

The value for the exponent variable n is known to vary between 2 for outdoors appli-

cations to slightly above 5 for some indoors settings. The exponent value is set to 2 in

all the simulation experiments in this work. The first order radio model, as given by

Equation 5.3, is deterministic. To capture the random nature of the radio channel, a

probabilistic component is added. This component will alter the received signal ran-

domly before delivering it to the destination. This is illustrated by the gray triangular

area in Figure 5.6.

Figure 5.6: Simulation analysis radio model.

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5.5 Performance Analyses

In this section, the results of the simulation experiments will be presented. The

first set will show performance results for the reliable protocol versus APTEEN using

the default parameters settings. These settings are given in Table 5.3. Results for this

first set of experiments are presented in Section 5.5.1.

The Hello message exchange will produce a non-negligible energy impact on the

proposed protocol. At the same time, the proposed protocol techniques will not be

activated until a certain number of Hello messages have been exchanged. A node will

send out one Hello message and one Hello-reply message per report period. To test the

protocol’s sensitivity to this Hello overhead, the second set of experiments will vary the

number of reports per round. This will affect the number of Hello messages sent per

round. This will ultimately affect how soon the energy saving techniques are activated.

Results from this set of experiments are presented in Section 5.5.2.

In Section 5.3.4, we presented optimizations to the Hello messages exchange.

One of the proposed optimizations is setting a limit to the number of Hello messages

exchanged per link. In the third set of experiments, we vary the number of Hello

messages and analyze its impact on the proposed protocol’s performance. Results for

these experiments set are presented in Section 5.5.3.

The default packet size used in all experiments is 300 Bytes, as shown in Table

5.3. This should be adequate for most applications. In the fourth set of experiments,

we test the proposed protocol’s performance when varying the packet size. This will

indirectly test the protocol’s scalability in handling heavy traffic levels. Another scal-

ability test we conduct is increasing the network size. Varying the packet size analysis

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is presented in Section 5.5.4.

Radio transceiver chip technology for wireless sensor networks is constantly im-

proving [17], [65], [21], [27], [22], [45]. Technical data sheets from various manufacturers

show different transmit to receive (Tx:Rx) power ratios. Earlier technologies show a

Tx:Rx ratio of around 2:1. Improvements in transceiver chip technology are towards

a smaller ratio. The simulation experiments default setting used for Tx:Rx ratio is

1:1, as shown in Table 5.3. In the fifth set of experiments, we test the effect of the

improvements in radio chip technology on the proposed protocol performance. In the

experiments of this set, three Tx:Rx ratios were compared, 2:1, 1:1 and 1:2. The results

of this set are given in Section 5.5.5.

Increasing the network size is the second scalability test. The first scalability

test is increasing the packet size. As shown in Table 5.3, the default number of nodes

is 100. In the sixth set of experiments, we increase the network size to 1600 nodes.

Results for this scalability test are presented in Section 5.5.6.

To test the MAC layer’s proposed Dynamic Backoff algorithm, the last set of

experiments create congested bandwidth conditions forcing the network to drop packets

due to full buffers. The default messages inter-arrival time is given in Table 5.3 as

1000 ∗ 106 nano seconds. This value was obtained through trial and error. It was

found to produce non-congested network utilization level. Message queues will hold

few messages, but no packets get dropped. For the MAC layer experiments, this value

was reduced to 1 ∗ 106 nano seconds. This creates a situation where all the nodes

buffers are full and the bandwidth is over-utilized. The network is forced to drop

packets, therefore, testing the effectiveness of the Dynamic Backoff algorithm at the

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MAC layer. The results of this set are given in Section 5.5.7.

Table 5.3: Simulation default parameter values.Parameter Value

Number of nodes 100

Number of cluster heads (CH) 5 (5%)

Number of simulation rounds 100

Number of reports per round 50

Packet size 300 Bytes

Messages inter-arrival time 1000 * 106 nano sec

Required Report messages reliability 70%

Reliability Required Control messages reliability 80%

Required Event messages reliability 90%

Minimum Hello exchange before using 10

the Link Rating parameter

Maximum number of Hello messages per link per 10

power setting

Initial (maximum) transmit power -77 dB

Hello messages Minimum transmit power (1) a -81 dB

Minimum transmit power (2) -83 dB

Minimum transmit power (3) -86 dB

Absolute minimum transmit power -90 dB

Power step size 0.5 dB

Retry limit for Report messages 2

MAC retry limit Retry limit for Control messages 3

Retry limit for Event messages 4

Retry limit for APTEEN messages 3

Transmit:Receive ratio (Tx:Rx) 1:1

Radio Node default transmit power -77 dB

Cluster head to base station (BS) 10 dB

transmit power

a the use of several values for the minimum transmit power is explained in Section 5.3.4

5.5.1 Proposed Protocol performance using Default Parameters

These are the main simulation experiments that measure the impact and effec-

tiveness of the proposed protocol. In this set, the simulation parameters were set as

given in Table 5.3. The performance parameters monitored are energy performance,

reliability and latency.

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5.5.1.1 Energy Performance

Figure 5.7 shows the energy performance of the proposed reliable protocol versus

APTEEN. The proposed protocol shows better energy performance before the 5th

round starts. From the graph, the gap between the two curves widens at around the

35th round. The reason is that the energy overhead due to the Hello messages exchange

the proposed protocol has incurred has been completely offset. After this point, the

energy savings that the protocol can achieve are clearly visible.

5.5.1.2 Reliability Performance

The reliability graphs for the report messages and the control messages are

shown in Figure 5.8 and Figure 5.9. These graphs show that the proposed protocol

has always exceeded the reliability requirements for the two message types. It is by

design that the proposed protocol has a lower reliability for these two message types.

It is this reliability relaxation that the proposed protocol uses to achieve the energy

savings shown in Figure 5.7.

The reliability graph for the event messages is shown in Figure 5.10. It shows the

proposed protocol has performed better than APTEEN. The proposed protocol takes

conservative approach to reliability. It does so by treating the application’s required

reliability values as constraints. The proposed protocol will use power settings that

are guaranteed to meet or exceed these values. As a result, it pursues event message

reliability for each event message reported. This coupled with higher retry limit at the

MAC layer has boosted the proposed protocol’s event reporting success rate. This is

while achieving better energy performance than APTEEN. This can be considered as

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Figure 5.7: Reliable protocol energy performance.

Figure 5.8: Report messages reliability.

key accomplishment of this work.

5.5.1.3 Latency Performance

The latency performance results are shown in Figure 5.11, Figure 5.12 and

Figure 5.13 for report messages, control messages and event messages, respectively.

Improving latency was not an objective for the proposed protocol. Nevertheless, the

effect of the proposed protocol on latency warrant studying to verify whether there

are any effects on latency. From the graphs, it is clear that under normal bandwidth

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Figure 5.9: Control messages reliability.

Figure 5.10: Event messages reliability.

utilization, the proposed protocol did not impact the latency performance. The latency

performance is revisited again later for congested network settings, and when the num-

ber of nodes in the network is increased. In these scenarios, the latency performance

is expected to degrade.

Figure 5.11: Report messages latency.

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Figure 5.12: Control messages latency.

Figure 5.13: Event messages latency.

5.5.2 Varying Number of Reports per Round

In these experiments, the number of simulation rounds is set to 40. The rest

of the simulation parameters are kept as given in Table 5.3, with the exception of

the number of reports per round, which is the parameter varied in this set. The aim

in conducting this set of experiment is to measure the impact of the Hello messages

overhead.


The energy performance results are shown in Figure 5.14. The graph shows that

for 10 Hello messages per round, the simulation was over before any energy savings can

be obtained. The two protocols have the same energy performance at around 20 reports

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per round. Energy performance gains are obtained when the number of reports was 30

reports per round or more. It is important to state here that if these experiments were

carried out for 100 rounds, the outcome would have been favorable for the proposed

protocol. In conduction this set of experiments using 40 simulation rounds, we show

the sensitivities of the proposed protocol. There is an overhead associated with the

Hello messages exchange. To benefit from using the proposed protocol, the network

must to be expected to function for a long time. Since this is the type of applications

the protocol is targeting, there is a substantial energy gain on the long run. The results

also indicate that the proposed protocol is not suitable for short-lived networks. In such

settings, the network operations will be over before the proposed energy optimization

techniques have a chance to absorb the Hello messages overhead.


Figure 5.15 and Figure 5.16 show the reliability performance of the proposed

protocol vs. APTEEN for report messages and control messages, respectively. Similar

to the default performance, the protocol did meet or exceeded the reliability constraints

for the report messages and for the control message. The event messages reliability

results are shown in Figure 5.17. The protocol’s conservative approach to reliability

resulted in better performance for the event messages.

5.5.3 Varying Maximum Number of Hello Messages

In this set, the variable parameter is the number of Hello messages per link for

each of the power levels. This was varied from 10 Hello messages to 50 Hello messages.

127

Figure 5.14: Varying number of reports per round energy performance.

Figure 5.15: Report messages reliability.

The number of simulation rounds was set to 40. The rest of the simulation parameters

were kept as given in Table 5.3.


The aim in conducting this set of experiments is to measure the impact of

the Hello messages overhead. The energy performance results are given in Figure

5.18. From the graph, an interesting result is obtained. Increasing the number of

Hello messages has a positive impact on the proposed protocol’s performance. The

expectations were that sending more Hellos will be taxing on the node’s energy and

therefore, will negatively impact the performance. The explanation was found after

128

Figure 5.16: Control messages reliability.

Figure 5.17: Event messages reliability.

careful study of the neighborhood tables in several nodes. The conservative approach to

link reliability meant that the communication power will be reduced only when enough

data justified dropping the power to a lower level. More Hello messages provided

the needed data for the power adjustment. Form the graph, it can be seen that the

energy performance improves in the range 20-30 Hello messages. It stays stable after

30 messages. This is consistent with the results obtained in separate experiments to

find the minimum number of Hello messages required before the link power settings

are stable. The results of that set were discussed in Section 5.3.4.

129

Figure 5.18: Impact of maximum number of Hello messages on energy performance.


The reliability results for the report, control and event messages are given in

Figures 5.19, 5.20 and 5.21, respectively. The reliability results were similar to the

default setting where the report and control messages did meet or exceed the reliability

constraints set by the application. The reliability results graph in Figure 5.21 shows

that the proposed protocol outperformed APTEEN for the event notification messages.

Figure 5.19: Report Messages Reliability.

5.5.4 Varying Packet Size

This is the first of two scalability experiments. The aim in conducting this set

is to evaluate how the proposed protocol scales to more data transfer. In this set,

130

Figure 5.20: Control Messages Reliability.

Figure 5.21: Event Messages Reliability.

the variable parameter is the packet size. Two sets of simulation experiments were

conducted. In the first set, normal packet sizes in the range 100 to 500 bytes were

used. WSNs communication is typically reporting sensed readings. This will typically

use small packet sizes. In the second set, larger packet sizes in the range 700 to 1100

Bytes were used. This set is aimed at showing the performance for a wide range of

message sizes. The number of simulation rounds was set to 40 rounds. The rest of the

simulation parameters were kept as given in Table 5.3.


The energy performance results are shown in Figure 5.22 for normal packet

sizes and in Figure 5.23 for large packet sizes. From the graphs, the performance of

131

the proposed protocol improved with increasing packet size. This results show that the

proposed protocol scales well and performs better with the increased amount of data

transferred.

Figure 5.22: Varying packet size energy performance, normal packet size.

Figure 5.23: Varying packet size energy performance, large packet size.


The reliability results for report messages, control messages and event messages

are shown in Figures 5.24 to 5.29 for different packet sizes . These results are for

both normal and large packet sizes. These results are consistent with the results

132

obtained in the previous sets. These graphs show that the proposed protocol has

satisfied the reliability constraints for the report messages and the control messages.

The protocol exceeded the reliability requirement for the report and control messages

and outperformed APTEEN for the event reporting messages.

Figure 5.24: Report messages reliability using normal packet sizes.

Figure 5.25: Report messages reliability using large packet sizes.

Figure 5.26: Control messages reliability using normal packet sizes.

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Figure 5.27: Control messages reliability using large packet sizes.

Figure 5.28: Event messages reliability using normal packet sizes.

5.5.5 Varying Transmit-Receive Energy Ratio

Improvements to radio transceiver for wireless sensor networks is an area of on-

going research [17], [65], [21], [27], [22], [45]. A few years ago, a review of technical data

sheets from several wireless sensor hardware manufacturers indicated that transmit to

receive power ratio is roughly 2:1. The trend has been towards smaller transmission

Figure 5.29: Event messages reliability using large packet sizes.

134

cost compared to receiving. To test the performance of the proposed protocol under

different radio hardware technologies, a set of experiments is conducted using different

transmit-to-receive power ratios. This set of experiments aims at testing the suitability

of the proposed protocol for future improved radio transceivers hardware. As shown in

Table 5.3, the default transmit:receive (Tx:Rx) ratio used throughout the simulations

is 1:1. In this set of experiments, transmit: receive (Tx:Rx) ratio of 1:2 and 2:1 were

tested.


The results in Figure 5.30 show the energy performance of the proposed protocol

vs. APTEEN, for Tx:Rx power ratio 1:2. The proposed protocol shows a clear energy

performance improvement compared to APTEEN. This energy improvement is even

better when the ratio is on the other extreme 2:1, as shown in Figure 5.31. Figure

5.32 shows a comparison between the three energy ratios 2:1, 1:1 and 1:2, at the end of

40 simulation rounds for each of the settings. The proposed protocol has consistently

shown better energy performance than APTEEN.

Figure 5.30: Energy performance using Tx:Rx ratio = 1:2.

135

Figure 5.31: Energy performance using Tx:Rx ratio = 2:1.

Figure 5.32: Energy performance using Tx:Rx ratio = 1:2 vs. 1:1 vs. 2:1.


The reliability results for the report and control messages for Tx:Rx ratios 1:2

and 2:1 are given in Figure 5.33, Figure 5.34, Figure 5.35 and Figure 5.36. These

results show that the proposed protocol has met or exceeded the required reliability

levels. The reliability results using the defaults parameters for these two message types

were given earlier in Figure 5.8 and Figure 5.9. The Tx:Rx ratio used there is 1:1. From

these results, we conclude that the proposed protocol has always met or exceeded the

reliability constraints for the report and control messages.

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For event reporting messages, the results for Tx:Rx ratios 1:2 and 2:1 are given

in Figure 5.37 and Figure 5.38, respectively. Adding these to the results for event

message’s performance for the ratio 1:1, given in Figure 5.10, we show that the proposed

protocol has outperformed APTEEN for event reporting, regardless of the Tx:Rx ratio.

Figure 5.33: Report messages reliability, Tx:Rx ratio = 1:2.

Figure 5.34: Report messages reliability for Tx:Rx ratio = 2:1.

5.5.6 Varying Network size

The first scalability test was performed by increasing the packet size. This was

discussed in Section 5.5.4. The second scalability test was performed by increasing the

network size. In these experiments, the number of nodes in the network is increased to

1600. As a result, the number of cluster heads (CH) is also increased to 80, to maintain

the 5% number of nodes to number of cluster heads ratio.

137

Figure 5.35: Control messages reliability for Tx:Rx ratio = 1:2.

Figure 5.36: Control messages reliability for Tx:Rx ratio = 2:1.


The energy performance graph is shown in Figure 5.39. The proposed proto-

col shows improved energy performance results compared to APTEEN. The gap in

energy performance widened between the 2nd and 3rd rounds. This is due to energy

performance gains offsetting energy loss caused by the Hello messages exchange. This

exchange stopped by the end of the first round and the overhead was completely offset

Figure 5.37: Event messages reliability for Tx:Rx ratio = 1:2.

138

Figure 5.38: Event messages reliability for Tx:Rx ratio = 2:1.

by the 3rd round. This shows that the proposed protocol scales well and gives even

better energy savings for bigger networks and for larger packet sizes.

Figure 5.39: Energy performance for large network (1600 nodes).


The reliability graphs for the report and control messages are shown in Fig-

ure 5.40 and Figure 5.41, respectively. The graphs show similar results to the cases

presented previously. When the network size is increased, the proposed protocol still

meets and exceeds the reliability constraint specified. The event messages reliability

results were similar or better than APTEEN’s as shown in Figure 5.42.

139

Figure 5.40: Report messages reliability performance.

Figure 5.41: Control messages reliability performance.


Figure 5.43, Figure 5.44 and Figure 5.45 show the latency performance of the

proposed protocol vs. APTEEN. Using the default parameters latency performance

is given in Figure 5.11, Figure 5.12 and Figure 5.13. Form these graphs, it can be

concluded that similar to the results obtained for the default parameters performance,

the proposed protocol has not impacted the latency when the network size is increased.

Figure 5.42: Event messages reliability performance.

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Figure 5.43: Report messages latency performance.

Figure 5.44: Control messages latency performance.

5.5.7 Varying Messages Inter-arrival Time

The objective of this set of experiments is to test the MAC layer’s proposed

Dynamic Backoff algorithm. This set of experiments aims at creating a congested

network that is forced to drop packets because of full buffers. The default report

messages inter-arrival time is given in Table 5.3 as 1000∗106 nano seconds. For this set

of experiments, this value was changed to 1∗106 nano seconds. This creates a situation

where the bandwidth is over-utilized and all the nodes buffers are full. The network

is forced to drop packets, therefore, testing the effectiveness of the Dynamic Backoff

algorithm at the MAC layer. The MAC retry for all message types for APTEEN is set

to be equal to the control messages’ MAC retry. Three different MAC retry limits are

141

Figure 5.45: Event messages latency performance.

tested 3, 4 and 6. For each of these APTEEN retry limits, a couple or more Dynamic

Backoff ratios were tested for the proposed protocol.

To aid in reading the graphs notations throughout this section, the notation

reliable protocol (x:y:z) means x= MAC retry limit for report messages, y= MAC

retry limit for control messages, z= MAC retry limit for event messages.


The energy performance graphs are given in Figure 5.46, Figure 5.47 and Figure

5.48 for APTEEN retry limit 3,4 and 6, respectively. From these graphs, the proposed

protocol has consistently performed better that APTEEN. It worth pointing out that

some retry ratios offer better energy performance than others. As an example, there is

a noticeable difference in the energy performance between reliable protocol with ratios

1:3:8 in comparison to reliable protocol curve with ratios 2:3:5 in Figure 5.46. Another

example is the energy performance difference between the reliable protocol’s curve for

the ratios 2:6:8 compared to the curve of the reliable protocol with ratios 2:6:12 in

Figure 5.48.

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Figure 5.46: Energy performance for different messages inter-arrival time, MAC de-fault retry limit= 3.



The results of the reliability performance for the periodic report messages are

shown in Figure 5.49, Figure 5.50 and Figure 5.51 for APTEEN retry limits 3, 4 and

6, respectively. From the graphs, and due to the high level of network traffic, the

reliability of these messages is low. This fact holds irrespective of the protocol used.

The periodic report messages’ reliability for the proposed protocol lagged a little behind

APTEEN. This is also the case for control messages, as shown in Figure 5.52, Figure

143


5.53 and Figure 5.54 for APTEEN retry limits of 3, 4 and 6 respectively. Nevertheless,

for the important event reporting messages, the proposed protocol assured high level

of messages delivery reliability. This is despite the challenge of an extremely congested

network conditions. These results are shown in Figure 5.55, Figure 5.56 and Figure

5.57 for APTEEN retry limits of 3, 4 and 6, respectively.

Assigning different MAC retry limits based on message type proved effective

in improving important message’s reliability. This performance has been consistent

in all experiment settings. Testing the effectiveness of the random backoff timers, on

the other hand, was inconclusive. Values that produced better reliability results in one

setting were at least ineffective or produced negative performance when e.g. the packet

size or the message queue size is changed. The test space and the number of variables

that affect this method suggest that this can be an investigation on its own.

144

Figure 5.49: Report messages reliability performance, APTEEN MAC default retrylimit= 3.



Figures 5.58 to 5.66 show the latency performance of the proposed protocol vs.

APTEEN. The default latency performance results were given in Figure 5.11, Figure

5.12 and Figure 5.13. Form the latency graphs presented here, it can be concluded

that except for the control messages, and similar to the results obtained for the default

parameters performance, the proposed protocol has not negatively affect the latency.

For the control messages, the latency is slightly increased as a result of event messages

staying alive longer in the nodes buffers. This fact increased the proposed protocol’s

event message’s reliability performance.

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Figure 5.52: Control messages reliability performance, APTEEN MAC default retrylimit= 3.

5.5.7.4 Impact of Different Retry Limits

The impact of varying the MAC retry limit is studied further using a stand-alone

simulation. This is to isolate the performance of the MAC retry limits from the complex

parameter space that the JiST/SWANS simulation tool imposes. In this setting, two

types of messages are defined, high reliability requirement (important) messages and


146


Figure 5.55: Event messages reliability performance, APTEEN MAC default retrylimit = 3.

low reliability requirement messages. One sender and one receiver are placed within

communication distance. The rate of sending the low reliability messages is made

variable. Figure 5.67 and Figure 5.68 show the results when both message types have

the same retry limits of 5 and 10, respectively. Under heavy network traffic loads, the

graphs show better reliability performance for the less important messages. This is due

to their higher sending rate. When the retry limits are different, 10 for the important

messages and 5 for the low reliability messages, the reliability performance is always

in favor of the important messages. This result is shown in Figure 5.69.

The higher retry limit has also enabled the important messages to have better

average number of retries. The results in Figure 5.70 show that for message interarrival

147



times 300, the important messages needed on average 9 retries. For the same data rate,

the low reliability messages reached their maximum retry limits.

Figure 5.58: Report messages latency performance, APTEEN MAC default retrylimit = 3.

148



5.6 Summary

Several simulation tests are developed to evaluate the performance of the pro-

posed protocol. Experiments covering a host of conditions are conducted to measure

their effect. These conditions included a default setting in which we tried as much as

possible to set the simulation parameters to typical WSNs operating conditions.

The protocol proposes a Hello messages exchange that introduces energy over-

head. To minimize this overhead, several optimization techniques are employed. The

Hello messages impact is measured through varying the maximum number of Hello

messages exchanged. Another test for the Hello messages impact is varying the num-

ber of Hello messages per round. This affects how soon the proposed protocol energy

149

Figure 5.61: Control messages latency performance, APTEEN MAC default retrylimit = 3.


optimization techniques will be activated. Results in this part show that some en-

ergy overhead is introduced. Nevertheless, this will be completely neutralized by the

proposed protocol’s energy optimization techniques. In the long run, better energy

performance is obtained. Results in this set also demonstrated that, based on our

approach, sending more Hello messages will result in better energy performance. This

is until all the communication power settings are stabilized. Beyond that point, any

additional Hello exchange will be ineffective in improving the energy performance, and

will only waste energy.

To test the proposed protocol scalability, two sets of experiments are conducted.

150


Figure 5.64: Event messages latency performance, APTEEN MAC default retry limit= 3.

In the first set, the packet size is varied. This is to test the protocol’s response to

more data traffic. In the second scalability set, the network size (number of nodes) is

increased. This test shows how the energy saving techniques scale to more communi-

cating entities in the network. The results obtained for these two sets revealed that

the proposed techniques scale well and produce better results for larger networks.

Variations in the radio transceiver technologies used in wireless sensor hardware

warrant testing the proposed protocol’s behavior under these different technologies.

Another set of experiments tested the proposed protocol for different transmit: receive

(Tx:Rx) power ratios. The conclusion reached in this set of experiments is that the

151



proposed protocol achieves energy savings and met the reliability constraints. This has

been the case regardless of the underlying radio hardware technology assumed.

The effectiveness of the proposed MAC layer Dynamic Backoff algorithms is

tested by creating congested network conditions. While the proposed techniques at the

routing and network layer were effective in improving energy performance, the MAC

layer proposed algorithms assured reliable delivery for important data. The proposed

protocol demonstrates the effectiveness of cross-layer design by ensuring important

data reliability, while delivering improved energy performance.

152

Figure 5.67: Low reliability vs. high reliability messages performance, messages retrylimit= 5.

Figure 5.68: Low reliability vs. high reliability messages performance, messages retrylimit= 10.

The proposed protocol shows better energy performance than the reference pro-

tocol APTEEN. This energy performance has been consistent regardless of the exper-

iment settings. Under normal network bandwidth utilization, the proposed protocol

has always met the reliability constraints, with no visible impact on latency. Also,

regardless of the experiment setting, the protocol has better reliability performance

than the reference protocol when handling important data.

Studying the effectiveness of assigning different backoff timers in JiST/SWANS

proved to be a substantial research on its own. This is due to the full-fledge simula-

tion environment and the host of variables the JiST/SWANS simulator imposes. This

153

Figure 5.69: Low reliability vs. high reliability messages performance, low messagesretry limits= 5 and 10.

Figure 5.70: Maximum number of message retries reached for both high and lowimportance messages.

coupled with the algorithm’s many sensitivities call for a separate study. A setup us-

ing a stand-alone simulation will isolate and control the unwanted effect of the many

variables existing in the JiST/SWANS environment. These variables can then be intro-

duced one at a time to study their impact of the random backoff timers. This also will

provide a chance to experiment with several possible variations of the backoff equation.

This is a possible and valid extension to the work carried out in this research.

154

Chapter 6

CONCLUSIONS AND FUTURE WORK

This chapter concludes the dissertation and considers future extensions to the

efforts presented.

6.1 Conclusions

Wireless sensor networks (WSNs) are one of the fastest developing new technolo-

gies. The availability of small, cheap low power embedded processors, radio transceivers

and sensors, integrated on a single chip is leading to the use of sensing, computing and

wireless communication for monitoring and interacting with the physical world.

A wireless sensor network (WSN) is a telecommunication network consisting

of spatially distributed sensors to monitor physical or environmental conditions in

a cooperative manner. Military applications such as monitoring of troop movement

and target tracking originally motivated the development of wireless sensor networks.

However, currently, wireless sensor networks are found in many civilian applications as

well.

As the wireless sensor networks research matures, it needs to move beyond

studies that are focused on studies that address the challenges of energy conservation

and resource constraints. To build trust in using these systems, more emphasis should

155

be placed on studying and analyzing the reliability and dependability of these systems.

So far, wireless sensor networks energy efficiency research has not taken reliability into

consideration as a performance parameter or as a design constraint. Two focus areas

in wireless sensor networks research can be identified. One area is concerned with

optimizing the energy performance and improving network lifetime. The second area

is focused on studying the WSNs reliability problem independent of the networking

and energy performance issues.

This work addresses communication reliability in the highly constrained wire-

less sensor networks environment. We propose a cross-layer, energy-efficient reliable

wireless sensor protocol design. The protocol benefits from the body of research in

the two areas of wireless sensors reliability and wireless sensors energy conservation.

The proposed protocol optimizes energy consumption while providing a reliable data

delivery network. The protocol introduces new energy saving techniques that consider

reliability as a design parameter and as a performance constraint. The protocol also

introduces a new medium access control layer (MAC) dynamic retry limit and dynamic

transmission power setting that are based on the messages reliability requirements.

Cross-layer design is defined as the interaction between the different stack layers


mance. It has been used in ad hoc wireless systems to improve throughput, latency,

and quality of service (QoS). Due to the severe energy constraints that are common

to wireless sensor networks operations, several publications have proposed cross-layer

design as an optimization technique. It has been argued that cross-layer designs can

surpass the performance of the best-optimized protocol whose techniques target a single

156

layer of the network stack. The improvements gained in performance come at a price.

This includes decreased architecture modularity and loss of the decoupling between

design and development. Also, cross-layer designs may be hard to debug, maintain

or upgrade. The interdependencies introduced need to be carefully considered and

evaluated to avoid the non-trivial problem of system’s instability.

Our proposed protocol uses cross-layer design as a performance and energy op-

timization technique. Nevertheless, the protocol avoids introducing layer interdepen-

dencies by preserving the stack architecture and optimizes the overall system energy

and reliability performance by information sharing. The information is embedded as

flags in the data and control messages that are moving through the stack. Each layer

reads these flags and adjusts its performance and handling of the message accordingly.

The performance of the proposed cross-layer protocol is evaluated using simula-

tion. An ad-hoc simulation tool is upgraded by adding wireless sensor networks mod-

eling capabilities and used in the evaluation. The performance is compared against the

APTEEN protocol. Results show that the proposed protocol produced better energy

performance, met reliability requirements and performed better than the reference pro-

tocol in the reliable delivery of the class of messages that are tagged as important or

critical data.

Several simulation tests are developed to evaluate the performance of the pro-

posed protocol. Experiments covering a host of conditions and parameters are con-

ducted to measure their effect. These conditions included a default setting in which we

tried as much as possible to set the simulation parameters to typical WSNs operating

conditions.

157

The proposed protocol introduces a Hello message exchange to gather statistics

for the communication link quality for the node’s one hop neighbors. This Hello mes-

sages exchange introduces an energy overhead. To minimize the effect of this overhead,

several optimization techniques are employed.

The Hello messages impact is measured through varying the maximum number of Hello

messages exchanged. Another test to the Hello messages impact is varying the num-

ber of Hello messages per round. This affects how soon the proposed protocol energy

optimization techniques will be activated. Results in this part show that some en-

ergy overhead is introduced. Nevertheless, this will be completely neutralized by the

proposed protocol’s energy optimization techniques. In the long run, better energy

performance is obtained. Results in this set also demonstrated that, based on our

approach, sending more Hello messages will result in better energy performance. This

is until all the communication power settings are stabilized. Beyond that point, any

additional Hello exchange will be ineffective in improving the energy performance, and

will only waste energy.

To test the proposed protocol scalability, two sets of experiments are conducted.

In the first set, the packet size is varied. This is to test the protocol’s response to

more data traffic. In the second scalability set, the network size (number of nodes) is

increased. This test shows how the energy saving techniques scale to more communi-

cating entities in the network. The results obtained for these two sets revealed that

the proposed techniques scale well and produce better results for larger networks.

Variations in the radio transceiver technologies used in wireless sensor hardware

warrant testing the proposed protocol’s behavior under these different technologies.

158

Another set of experiments tested the proposed protocol for different transmit: receive

(Tx:Rx) power ratios. The conclusion reached in this set of experiments is that the

proposed protocol achieves energy savings and met the reliability constraints. This has

been the case regardless of the underlying radio hardware technology assumed.

The effectiveness of the proposed MAC layer Dynamic Backoff algorithms is

tested by creating congested network conditions. The improvements that the cross-

layer design approach can provide are evident in the proposed protocol. While the

proposed techniques at the network layer proved very effective in improving energy

performance, the MAC layer proposed algorithms assured reliable delivery for impor-

tant data. The proposed protocol demonstrates the effectiveness of cross-layer design

by ensuring important data reliability, while delivering improved energy performance.

The proposed protocol shows better energy performance than the reference pro-

tocol APTEEN. This energy performance has been consistent regardless of the experi-

ment settings. Under normal network bandwidth utilization, the proposed protocol has

always met the reliability constraints, with no visible impact on latency. Also, regard-

less of the experiment setting, the proposed protocol has better reliability performance

than the reference protocol when handling important data.

6.2 Future Work

This work is a first effort into combining multiple research proposals into one

deployable and practical solution. A great deal of work is still ahead and needed in

this direction. The following lists few opportunities:

• The Hello messages are instrumental in achieving the energy savings that the

159

proposed protocol enjoys. These energy savings are successfully in neutralizing

the overhead that the Hello messages produced. This Hello exchange gives the

nodes a local view of its neighbors. This view can be expanded as needed to

make the node aware of neighbors few hops away. This is possible by increasing

the amount and type of information that the Hello messages carry. Nodes will

then become aware of their expanded surroundings. This fact can be used to

assist with other networking challenges. There are many possibilities where this

exchange can provide a solution, an example is obtaining a balanced distribution

of cluster head (CH) nodes through the network. As it stands, all hierarchical

wireless sensor networks routing protocol use distributed cluster head selection

algorithms. These algorithms target, but do not guarantee uniform cluster heads

distribution.

• The results of the proposed MAC layer retry limit performance show some of the

proposed protocol ratios had better energy performance than others. This fact

needs more investigation into the exact behavior so more energy savings can be

achieved while ensuring that the reliability gains are not affected.

• Throughout the experimentation modeling and simulation phases, nodes are

given enough initial energy to stay alive till the end of the simulation rounds.

An investigation can be carried out for the effect of nodes consuming all their

energy resources, being removed from the network and fresh nodes being added.

Possible modifications to the proposed protocol to fit this more realistic view of

the network can be investigated.

160

• Studying the effectiveness of assigning different backoff timers in JiST/SWANS

proved to be a substantial research on its own. This is due to the full-fledge

simulation environment and the host of variables in the JiST/SWANS simulator.

This coupled with the algorithm’s many dependencies call for a separate study.

A setup using a stand-alone simulation will isolate and control the unwanted

effect of the many variables existing in the JiST/SWANS environment. These

variables can then be introduced one at a time to study their impact on the

random backoff timers. This also will provide a chance to experiment with several

possible variations of the backoff equation. This is a possible and valid extension

to the work carried out in this dissertation.

161

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