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April 3, 2008 (c) Ankit Agarwal 1

University of Kansas | School of Engineering

Department of Electrical Engineering

and Computer Science

Multi-hop Medium Access Control for WSNs:An Energy Analysis Model

Ankit AgarwalEECS Student

Electrical Engineering and Computer Science

University of Kansas

2001 Eaton Hall

Lawrence, KS – 66045

E-Mail: [email protected]





Outline

1. Introduction

2. Related Work

3. Protocol descriptions: np-CSMA, S-MAC, NanoMAC

4. Baseline Multi-hop Communication Model

5. Energy Consumption Model with MAC

6. Regular Sleep Periods

7. Multi-hop Analysis

8. Multi-hop with Random Spacing

9. Conclusions





Outline

1. Introduction

2. Related Work







9. Conclusions





Introduction

• Wireless Sensor networks (WSNs) have become very popular due to cheap single-chip transceivers and micro-controllers.

• They have many applications: military, environment and habitat monitoring, healthcare apps., home automation and traffic control

• Researchers have come up with various energy-efficient non-persistent MAC protocols: np-CSMA, S-MAC, NanoMAC

• Compare the energy consumption in above protocols using an energy consumption model.





Introduction (contd.)

• J. Haapola, Z. Shelby, C. Pomalaza-Raez, P. Mahonen; “Multi-hop Medium Access Control for WSNs: An Energy Analysis Model”; EURASIP Journal on Wireless Communications and Networking2005:4, 523-540

• Propose: An energy consumption model for transmission and reception of MAC frames, develop a coordinated sleep group energy consumption model, and analytically investigate the effect of sleep on sensor networks [1]

• Show that although in an ideal scenario multi-hop communications perform better than single-hop, realistic energy models and MAC design have significant impact

• Main metric used is absolute energy consumption per uselfulsuccessfully transmitted bit





Outline

1. Introduction

2. Related Work







9. Conclusions





Related Work

• Radio Modeling:

– In [2], the authors present an energy consumption model and optimal

packet payload sizes for various channel bit error rates (BERs) and

coding schemes are determined.

– In [3, 4] a linear radio model is presented as seen in Figure 1 for multi-

hop analysis. It also presents an optimal hop distance characteristic for

multi-hop communications.

– In [5], the authors present a single-hop radio energy consumption model

J. Haapola, Z. Shelby, C. Pomalaza-Raez,

P. Mahonen; “Multi-hop Medium Access

Control for WSNs: An Energy Analysis

Model”; EURASIP Journal on Wireless

Communications and Networking 2005:4,

523-540





Related Work

• Topology and network protocols:

– Protocols like LEACH [3], SPIN [6], data funneling [7] and directed

diffusion [8] are some of the protocols that take energy-efficiency into

account

– LEACH builds dynamic clusters to make sure that most nodes need to

transmit to short distances

– In SPIN, sensor nodes advertise data to those nodes only that have

interest in the data from them

– In data funneling, border nodes do the multi-hop data transfer to the

sink node

– In directed diffusion, the sink node broadcasts what data it needs and

sets up gradients to nodes that have the data

– All the above protocols are data-centric and can be modeled as a

network shown in Figure 1





Related Work

• Cross-layer studies:

– Authors in [9] discuss MAC-routing protocol cross-layer study for ad-hoc

networks

– It does not take energy into account

– It shows the importance of considering different layers when designing a

new protocol

• Medium Access Control:

– Energy-efficient protocols like PAMAS, S-MAC, MACAW, T-MAC,

NanoMAC and np-CSMA developed

– These protocols are modifications from the traditional ad hoc networking

– They have inherent flaws for sensor networks





Outline

1. Introduction

2. Related Work







9. Conclusions





Protocol Descriptions

• Non-persistent CSMA (np-CSMA): [10]

– Node senses channel when it has to send data (CS)

– If channel not vacant, back off for a random time before CS again

– If channel found vacant, transmit data

– Wait for an ACK frame from intended recipient

– If ACK received before timeout, data successfully received

– Else data needs to be re-transmitted

– Here, the ACK frame is transmitted on the same channel as data






• S-MAC: [11]

– Operation and frame divided into two periods: active and sleep period

– During sleep period, all nodes sharing same schedule sleep

– Active period sub-divided into SYNC and Request-to-send (RTS) period

– Message Passing: When network layer has packet size larger than a

single frame to transmit, S-MAC breaks them down in to smaller pieces

and transmits them as a burst of consecutive data

– Overhearing nodes sleep during data transfer

– If data transmission continues beyond active period, S-MAC can

prolong the time that the nodes are awake.






• NanoMAC: [12, 13]

– p-non persistent

– With probability p, the protocol will act non-persistent

– With probability (1-p), the protocol will refrain from sending even before

CS and re-schedule a time to attempt transmitting

– Nodes sleep during random contention window

– The CS is relatively short for nanoMAC

– In RTS/CTS frames NanoMAC does virtual carrier sensing





Outline

1. Introduction

2. Related Work







9. Conclusions





Baseline Multi-hop Communication Model

• The simple linear multi-hop model used in this analysis is shown in Figure 2 below:

• Power Consumption Model is shown in Figure 3 below:

J. Haapola, Z. Shelby, C. Pomalaza-Raez, P. Mahonen; “Multi-

hop Medium Access Control for WSNs: An Energy Analysis

Model”; EURASIP Journal on Wireless Communications and

Networking 2005:4, 523-540

J. Haapola, Z. Shelby, C. Pomalaza-Raez, P. Mahonen; “Multi-

hop Medium Access Control for WSNs: An Energy Analysis

Model”; EURASIP Journal on Wireless Communications and

Networking 2005:4, 523-540






• Simple multi-hop communication model without MAC

• In [2] a model for radio power consumption is given for energy per bit eb as

where

etx and erx are transmitter and receiver power consumptions per bit

Edec is the energy required for decoding a packet.

ι is the payload length in bits

• etx with optimal power control can represented as follows:

where

ete is the energy consumption of the transmitter electronics per bit

eta is the energy consumption of the transmit amplifier per bit over distance of 1 m; d is transmission distance; α is the path loss exponent






• Expression for eta from [7] is as follows:

• where

(S/N)r is the desired signal-to-noise ratio at receiver’s demodulator

NFRx is the receiver noise figure

N0 is the thermal noise floor for 1Hz bandwidth,

BW is the channel noise bandwidth,

λ is the wavelength in meters,

Gant is the antenna gain,

ηamp is the transmitter efficiency, and

Rbit is the raw channel rate in bits per second.






• The characteristic distance dchar from [7] is a radio specific parameter that describes when the energy consumptions of the transmitter and receiver circuitries are in balance with that of the transmitter amplifier. It is mathematically defined as:

Table 1 gives the radio specific

parameters that were used. From this

table dchar was found to be 31.5m with

a BER of 10-4 assuming non-coherent

FSK modulation

J. Haapola, Z. Shelby, C. Pomalaza-Raez, P.

Mahonen; “Multi-hop Medium Access Control

for WSNs: An Energy Analysis Model”;

EURASIP Journal on Wireless Communications

and Networking 2005:4, 523-540






• Multi-hop power consumption

– The total energy consumed in the network by each node

transmitting their own frames and forwarding for others is:

(Derived in [8])

– Comparing this to the single-hop case where the node transmits directly

to the sink node, Energy consumed in the network is given as:

(Derived in [8])





Baseline Multi-hop Communication Model• Baseline results shown in Figure 4 and 5.

J. Haapola, Z. Shelby, C. Pomalaza-Raez, P. Mahonen; “Multi-hop Medium Access Control for WSNs: An Energy Analysis Model”;

EURASIP Journal on Wireless Communications and Networking 2005:4, 523-540





Outline

1. Introduction

2. Related Work







9. Conclusions





Energy Consumption Model (ECM) with MAC

• Transmit Energy

– ECM for transmission is shown in Figure 6.

– It has four different states: Arrive, Backoff, Attempt, and Success

Figure 6: Transmit energy model for nanoMAC. The arrows present energy consuming transitions from

one state to a new state while the states are instant and do not consume energy. Pb, Pers, Ps, and Pc are

transition probabilities.








• Transmit Energy (contd.)

– Let ETX be the energy consumed by a node when a packet arrives at it

in the ‘Arrive’ state and is succesful

– Let E(A) be the avg. energy consumed when a node visits ‘Attempted’

state

– Let E(B) be the energy consumed when a node visits ‘Backoff’ state

– Then from [1]






• Receive Energy

– ECM for receive is shown in Figure 6.

– It has three different states: Idle, Reply, and Received








• Receive Energy (contd.)

– Let ERX be the energy consumed by

a node from listening for a

transmission to detecting and receiving

a valid packet

– Then from [1]

• The avg. energy per useful bit is

shown in Figure 8

J. Haapola, Z. Shelby, C. Pomalaza-Raez, P. Mahonen; “Multi-hop

Medium Access Control for WSNs: An Energy Analysis Model”;

EURASIP Journal on Wireless Communications and Networking

2005:4, 523-540





Outline

1. Introduction

2. Related Work







9. Conclusions





Regular Sleep Periods

• More realistic – Include periods when there is no data communication

ongoing as well as sleeping to save energy

• The data arrival rate to the system is Poisson distributed and in Table 2 we

can see the relevant parameters for the data packet communications.








• NanoMAC Sleep Groups

– Four-level sleep scheduling

– Operated at cycle of 9.6 seconds

– Each control frame has 1-octet sleep field which is sub-divided into two parts:

• Sleep group: SG 00 (No sleep periods), SG 01 (nodes wake up every 0.4 seconds), SG 10 (nodes wake-up every 0.96 seconds), SG 11 (nodes with 1.6 seconds wake-up time)

• Next wake-up: This field indicates the next time the node will wake up for comunications

– The above values are carefully selected examples. They can have other values.

– The worst-case energy consumption with sleep Ewcs is given by following in [1]:






• The total energy consumption per useful transmitted bit in the worst-case

scenario are given below:

J. Haapola, Z. Shelby, C.

Pomalaza-Raez, P. Mahonen;

“Multi-hop Medium Access

Control for WSNs: An Energy

Analysis Model”; EURASIP

Journal on Wireless

Communications and Networking

2005:4, 523-540





Outline

1. Introduction

2. Related Work







9. Conclusions





Multi-hop Analysis

• Three different sleep scheduling considered:

– Perfect sleep schedule

– Multi-group sleep schedule

– Common sleep schedule

• Figures 10, 11, and 12 display energy consumption behavior, all three MAC

protocols with uniform optimum spacing, and uniform non-optimal spacing

respectively.





Multi-hop Analysis







Multi-hop Analysis


Mahonen; “Multi-hop Medium Access Control for

WSNs: An Energy Analysis Model”; EURASIP

Journal on Wireless Communications and

Networking 2005:4, 523-540





Outline

1. Introduction

2. Related Work







9. Conclusions





Multi-hop with Random Spacing

• Adopt new communication styles:

– Shortest hop (former multi-hop)

– Longest hop (single-hop communications)

• Figures 13, 14, and 15 display the energy consumption behavior in random

scenarios, when α = 4, and varying path loss respectively.







Mahonen; “Multi-hop Medium Access Control for

WSNs: An Energy Analysis Model”; EURASIP

Journal on Wireless Communications and

Networking 2005:4, 523-540





Outline

1. Introduction

2. Related Work







9. Conclusions





Conclusions

• Described the working of energy-efficient protocols: np-CSMA, S-MAC, and

NanoMAC

• Compared the above protocols using an energy analysis model

• Gained insight for when to use multi-hop communications instead of single-

hop

• Well-designed sensor MAC protocol comes very close to being ideal, only

the absolute value energy consumption is higher, on the order of one mag.

• There are some inherent flaws in adapting existing ad hoc MAC protocols to

sensor networks e.g. Idle listening and overhearing

• Introducing regular sleep periods can have major impact on energy

consumption of nodes

• Several factors affect design of sensor networks:

– Environment of operation

– Availability of power control on transmitter amplifier

– If delay is not a concern, reduce the amount of time to listen

– Transceiver’s radio parameters highly influence the system energy performance





References[1] J. Haapola, Z. Shelby, C. Pomalaza-Raez, P. Mahonen; “Multi-hop Medium Access Control for

WSNs: An Energy Analysis Model”; EURASIP Journal on Wireless Communications and Networking 2005:4, 523-540

[2] Y. Sankarasubramaniam, I. F. Akyildiz, and S.W.McLaughlin, “Energy efficiency based packet size optimization in wireless sensor networks,” in Proc. 1st IEEE International Workshop on Sensor Network Protocols and Applications (SNPA ’03), pp. 1–8, Anchorage, Alaska, USA, May 2003.

[3] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks,” in Proc. 33rd Annual Hawaii International

Conference on System Sciences (HICSS ’00), vol. 2, pp. 1–10, Maui, Hawaii, USA, January 2000.[4] P. Chen, B. O’Dea, and E. Callaway, “Energy efficient system design with optimum transmission

range for wireless ad hoc networks,” in Proc. IEEE International Conference on Communications (ICC ’02), vol. 2, pp. 945–952, New York, NY, USA, April-May 2002.

[5] R. Min, M. Bhardwaj, N. Ickes, A. Wang, and A. Chandrakasan, “The hardware and the network: total-system strategies for power aware wireless microsensors,” in Proc. IEEE CAS Workshop on Wireless Communications and Networking, Pasadena, Calif, USA, September 2002.

[6] W. R. Heinzelman, J. Kulik, and H. Balakrishnan, “Adaptive protocols for information dissemination in wireless sensor networks,” in Proc. 5th Annual ACM/IEEE International Conference on MobileComputing and Networking (MobiCom ’99), pp. 174–185, Seattle,Wash, USA, August 1999.

[7] D. Petrovic, R. C. Shah, K. Ramchandran, and J. Rabaey, “Data funneling: routing with aggregation and compression for wireless sensor networks,” in Proc. 1st IEEE International Workshop on Sensor Network Protocols and Applications (SNPA ’03), pp. 156–162, Anchorage, Alaska, USA, May 2003.

[8] C. Intanagonwiwat, R. Govindan, and D. Estrin, “Directed diffusion: a scalable and robust communication paradigm for sensor networks,” in Proc. 6th Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom ’00), pp. 56–67, Boston, Mass, USA, August 2000.





References (contd.)

[9] E. M. Royer, S.-J. Lee, and C. E. Perkins, “The effects of MAC protocols on ad hoc

network communication,” in Proc. IEEE Wireless Communications and Networking

Conference (WCNC ’00), vol. 2, pp. 543–548, Chicago, Ill, USA, September 2000.

[10] L. Kleinrock and F. A. Tobagi, “Packet switching in radio channels: Part I—Carrier

sense multiple-access modes and their throughput-delay characteristics,” IEEE

Trans. Commun., vol. 23, no. 12, pp. 1400–1416, 1975.

[11] W. Ye, J. Heidemann, and D. Estrin, “Medium access control with coordinated

adaptive sleeping for wireless sensor networks,” IEEE/ACM Trans. Networking, vol.

12, no. 3, pp. 493–506, 2004.

[12] J. Haapola, “Low-power wireless measurement system for physics sensors,”

Master’s thesis, Department of Physical Sciences, University of Oulu, Oulu, Finland,

2002, unpublished, available online on http://www.ee.oulu.fi/∼jhaapola/.

[13] J. Haapola, “NanoMAC: a distributed MAC protocol for wireless sensor networks,” in

Proc. 18th Convention on Radio Science & IV FinnishWireless Communication

Workshop (FWCW ’03), pp. 17–20, Oulu, Finland, October 2003.

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