reliable and energy-efficient communication in wireless sensor networks

Computer Science Colloquium Western Michigan University

Reliable and Energy-Efficient Communication in Wireless Sensor Networks

Torsten Braun, Universität Bern, [email protected], cds.unibe.ch

joint work with Philipp Hurni

mailto:[email protected]

http://www.iam.unibe.ch/

Torsten Braun: Reliable and Energy-Efficient Communication in Wireless Sensor Networks

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Overview

> Introduction— Wireless Sensor Network Applications and Application Requirements— Design, Implementation, Evaluation of WSN Protocols

> Experimentation Platform for WSN Research— Wireless Sensor Network Testbed — Software-Based Estimation of Energy Consumption

> WSN Research Experiments— Traffic-Adaptive and Energy-Efficient WSN MAC Protocol— Adaptive Forward Error Control in WSNs— TCP Performance Optimizations for WSNs

> Conclusions

Kalamazoo, June 13, 2012


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Wireless Sensor Network Applications

> Monitoring and control of buildings using sensor nodes and artificial neural networks

Markus Wälchli, Torsten Braun: Building Intrusion Detection with a Wireless Sensor Network, ICST AdHocNets, Niagara Falls, 2009



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Sion

Sierre

Tseuzierstorage lake

Plaine Morte glacier

Wireless Sensor Network Applications

> Environmental monitoring (A4-Mesh, a4-mesh.unibe.ch)

Almerima Jamakovic, Torsten Braun, Thomas Staub, Markus Anwander: Authorisation and Authentication Mechanisms in Support of Secure Access to WMN Resources, IEEE HotMesh, San Francisco, June 2012


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https://a4-mesh.unibe.ch/


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Application Requirements

> Energy-efficient operation> Low delays> Reliability> Adaptivity to varying link characteristics and traffic load



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Design, Implementation, and Evaluation of Wireless Sensor Network Protocols

> Simulations are only meaningful with accurate calibration of parameters, e.g., energy consumption, transmission characteristics, traffic models.

> Experiments in testbeds give insights about protocol behaviour in more realistic scenarios and system-related issues, but face several problems— Experiment control— Scalability— Reproducability— Energy measurements— Mobility


Wireless Sensor Network Testbed


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Wisebed WSN Testbed @ Universität Bern

> Wisebed: EU FP7 project, 2008 - 2011> Approx. 50 TelosB/MSB430 nodes connected to portal via Ethernet


Ethernet

Mesh Node

Portal

InternetUSBLANwireless

Sensor Node


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TARWIS Experiment Configuration



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TARWIS Experiment Monitoring



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TARIWS-Generated Experiment Trace


Software-Based Estimation of Energy Consumption


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Software-Based Estimation of Energy Consumption

> Problem: Equipment of sensor nodes with measurement hardware is — very expensive.— difficult in out-door environments / real-world deployments.— not sufficient to support energy awareness.

– Energy awareness: Application / system adapts operation to meet energy consumption constraints.

> Solution: Software-based energy measurement (calibration of software-based model using measurement hardware)



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Hardware-Based Energy Measurements

> Measurement of current draw and voltage using Sensor Network Management Devices (SNMD) from KIT



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Simple 3-State-Model


A. Dunkels, F. Osterlind, N. Tsiftes, Z. He: Software-based On-line Energy Estimation for Sensor Nodes. IEEE EmNets, 2007


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Measured vs. Estimated Energy Consumption

Approach: Measurement of current draw in different states and energy estimation by



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3-State-Model with State Transitions


Revised estimation:


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Estimation Accuracy


OLS: Ordinary Least Squares Regression Analysis

On the Accuracy of Software-based Energy Estimation Techniques. Philipp Hurni, Torsten Braun, Benjamin Nyffenegger, Anton Hergenroeder: 8th European Conference on Wireless Sensor Networks (EWSN), Bonn, Germany, February 2011.

MaxMAC: Maximally Traffic-Adaptive and Energy-Efficient WSN MAC Protocol


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WiseMAC

> Very energy-efficient MAC protocol, but adaptivity to traffic variation is very limited.

> Unsynchronized nodes wakeup for a short time> Tpreamble = min {4θL,T}

— θ: clock drift, L: time since last update, T: duration of a cycle> „Piggybacking“ of wakeup times

Enz et al.: WiseNET: An Ultralow-Power Wireless Sensor Network Solution, IEEE Computer, Vol. 37, No. 8; August 2004



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MaxMAC: a Maximally Traffic-Adaptive and Energy-Efficient WSN MAC Protocol

> is based on sampling of preambles, cf. WiseMAC> Additional wakeups for higher rates of received packets

(measurement by sliding window)— Periodic reports in acknowledgements from receiver to sender— State transitions if thresholds T1,T2,TCSMA are exceeded.

Base state

S12 *

duty cycle

S24 *

duty cycle

CSMA

RECV

packet rate ≥ T1 packet rate ≥ T2 packet rate ≥ TCSMA

packet rate < T1

Lease expiredpacket rate < T2

Lease expiredpacket rate < TCSMA

Lease expiredKalamazoo, June 13, 2012


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MaxMAC

CSMA

Philipp Hurni and Torsten Braun. MaxMAC: a maximally traffic-adaptive MAC protocol for wireless sensor networks. 7th European Conference on Wireless Sensor Networks (EWSN), Coimbra, Portugal, February 2010.



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MaxMAC Implementation on MSB430

> Threshold parameters: T1 = 1, T2 = 2, TCSMA = 3 packets / s> Base duty cycle: 0.6 % (3 ms) for a base interval of 500 ms> Frame size: 40 bytes including header> Lease times: 3 s > Bit rate: 19.2 kbps> Implementation of packet burst mode



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Experiments with Intruder Scenario I


WiseMAC

MaxMAC

CSMA


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Experiments with Intruder Scenario II


Adaptive Forward Error Correction


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Error Control in Wireless Sensor Networks

> Wireless channels in sensor networks have varying bit error rates, sometimes up to 20 %.

> Options— Automatic Repeat Request (ARQ)

– Retransmission adds delay.– Original transmission was useless, but consumed bandwidth and

energy.

— Forward Error Correction (FEC)– Relatively small delay (due to encoding and decoding) compared to

ARQ for error correction – En-/decoding can be costly (several 100 ms for decoding).– Too strong codes consume computing resources and bandwidth. – Too weak codes might not be able to correct errors.

> Proposed Approach: Adaptive FEC



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Implementation of FEC Library


> Repetition Code> Hamming Code> Double Error Correction Triple Error Detection (DECTED)> Bose-Chaudhuri-Hocquengham (BCH)


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Adaptive FEC


> Stateful Adaptive FEC (SA)— Selection of current code dependent on success of previous transmission

(next higher / lower level)— Quick adaptation

> Stateful History Adaptive (SHA)— History of last transmissions (here: 5)— For successful/failed transmissions: storage of next lower/higher level— Selection of level with majority in history— Reacts less quickly than SA-FEC

> Stateful Sender Receiver Adaptive (SSRA)— Consideration of number of corrected bit errors

by receiver (to be reported in acknowledgement)

(63,36)

Philipp Hurni, Sebastian Barthlomé, Torsten Braun: Link-Quality Aware Run-Time Adaptive Forward Error Correction Strategies in Wireless Sensor Networks, submitted


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Energy Consumption by FEC and ARQ

> Additional power consumption by FEC> In case of no FEC, MSB430 node can enter lower power mode

with Idefault

> Energy for encoding/decoding 32 bytes (30/100 ms): 0.95 mJ> Energy for retransmission



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Wisebed Experiments

> Different link characteristics → Deployment of a single FEC scheme would not be most efficient.



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Static vs. Adaptive FEC


> Better error correction performance of adaptive FEC schemes than for static ones.

> Adaptive FEC advantages— Lower processing and energy costs— Lower bandwidth and lower interference

in multi-hop scenarios— Higher packet delivery rate— Adapt automatically to different

link characteristics

TCP Performance Optimizations forWireless Sensor Networks


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Reasons for Poor TCP Performance in Wireless Multi-Hop Networks

> Higher bit error rates and packet loss> Underlying MAC protocols

(exponential back-off, hidden / exposed nodes)> TCP end-to-end error and congestion control mechanisms


TCP data segment loss TCP acknowledgement loss


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Optimization of TCP in WSNs

> Distributed TCP Caching (Dunkels et al., 2004)

> TCP Support for Sensor Networks (Braun et al., 2007)


Adam Dunkels, Thiemo Voigt, and Juan Alonso. Making TCP/IP Viable for Wireless Sensor Networks. 1st European Workshop on Wireless Sensor Networks (EWSN 2004)

Torsten Braun, Thiemo Voigt, Adam Dunkels. RCP Support for Sensor networks. IEEE/IFIP WONS 2007.


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Caching and Congestion Control (cctrl) Module

> is aware of all TCP packets forwarded by a node by interception of outbound packets.

> allocates buffer for 2 packets per TCP connection (1 for each direction, µIP has max. 1 unacknowledged TCP data segment per connection)


Philipp Hurni, Ulrich Bürgi, Markus Anwander, Torsten Braun: TCP Performance Optimizations for Wireless Sensor Networks, 9th European Conference on Wireless Sensor Networks (EWSN), Trento, Italy, February 2012


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cctrl Functions

> Caching of — complete TCP data segments and scheduling of retransmission timer (RTO = 3 ∙

RTTestimated, RTTestimated = estimated RTT between intermediate node and destination)— TCP/IP header for TCP acknowledgements

> Local retransmission of TCP data segment (max. 3 attempts), when RTO expires prior to TCP acknowledgement reception (a)

> Removal of TCP data segments, if acknowledgement number of TCP acknowledgement > sequence number of cached TCP data segment

> For retransmitted TCP data segments, for which a TCP acknowledgement has been received: discard TCP data segment; regenerate TCP acknowledgement (b)



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Channel Activity Monitoring


> MAC proxy notifies cctrl upon reception of any packet and stores a timestamp in activity history.

> cctrl continuously calculates channel activity level (= # overheard packets by MAC proxy during the last time period RTTestimated)

> Observation:— Channel activity level of most nodes = 0 during long idle periods— Long idle periods by

– TCP data segment loss at one of the first hops – TCP acknowledgement loss close to its destination

(i.e. TCP data segment’s source).

> Approach: — Split RTO into:

– RTO1 = 3 ∙ RTTestimated ∙ 2/3

– RTO2 = 3 ∙ RTTestimated ∙ 1/3

— When RTO1 expires: early retransmission, if channel activity level = 0; otherwise: retransmission when RTO2 expires.

— Triggers early local retransmissions close to destination


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Long Idle Periods



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Spatial Reuse by Multiple TCP Connections



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Testbed Experiments

> 7 TelosB nodes in different rooms of a 3 floor building using U Bern’s Wisebed testbed

> Receiver node 1> Sender nodes 2-7> Experiments with different

MAC protocols for 10 minutes, 15 repetitions

> 16 bytes payload> 79 bytes per TCP data segment> 63 bytes per TCP

acknowledgement> Total: approx. 2500 experiments

during > 400 hours



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Overall Comparison of Throughput



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Overall Comparison of Energy Consumption



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Conclusions

> Contributions— Design and experimental evaluation of energy-efficient, reliable, and

adaptive protocols > Experiences: Development and use of WSN testbed resulted in

— More efficient use of hardware resources— Testbed experiments as easy as simulations— Repeatability and larger number of experiments

(statistical significance)— Reproducability of experiments and results

> Outlook— Integration of wireless mesh nodes into testbed architecture— Mobility support— Multimedia sensor networks— Radio sensor networks



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Thanks for your attention !

> [email protected] > http://cds.unibe.ch


mailto:[email protected]

http://cds.unibe.ch/

reliable and energy-efficient communication in wireless sensor networks

Technology

energyefficient communication

wireless sensor networks

energy awareness

online energy estimation

energy consumption constraints

equipment of sensor

kit kalamazoo

wsnsconclusions kalamazoo