reliable and energy-efficient communication in wireless sensor networks
DESCRIPTION
CS Colloquium Western Michigan UniversityTRANSCRIPT
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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
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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
Kalamazoo, June 13, 2012
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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
Kalamazoo, June 13, 2012
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Application Requirements
> Energy-efficient operation> Low delays> Reliability> Adaptivity to varying link characteristics and traffic load
Kalamazoo, June 13, 2012
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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
Kalamazoo, June 13, 2012
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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
Kalamazoo, June 13, 2012
Ethernet
Mesh Node
Portal
InternetUSBLANwireless
Sensor Node
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TARWIS Experiment Configuration
Kalamazoo, June 13, 2012
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TARWIS Experiment Monitoring
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TARIWS-Generated Experiment Trace
Kalamazoo, June 13, 2012
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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)
Kalamazoo, June 13, 2012
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Hardware-Based Energy Measurements
> Measurement of current draw and voltage using Sensor Network Management Devices (SNMD) from KIT
Kalamazoo, June 13, 2012
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Simple 3-State-Model
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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
Kalamazoo, June 13, 2012
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3-State-Model with State Transitions
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Revised estimation:
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Estimation Accuracy
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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.
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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
Kalamazoo, June 13, 2012
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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.
Kalamazoo, June 13, 2012
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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
Kalamazoo, June 13, 2012
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Experiments with Intruder Scenario I
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WiseMAC
MaxMAC
CSMA
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Experiments with Intruder Scenario II
Kalamazoo, June 13, 2012
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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
Kalamazoo, June 13, 2012
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Implementation of FEC Library
Kalamazoo, June 13, 2012
> Repetition Code> Hamming Code> Double Error Correction Triple Error Detection (DECTED)> Bose-Chaudhuri-Hocquengham (BCH)
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Adaptive FEC
Kalamazoo, June 13, 2012
> 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
Kalamazoo, June 13, 2012
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Wisebed Experiments
> Different link characteristics → Deployment of a single FEC scheme would not be most efficient.
Kalamazoo, June 13, 2012
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Static vs. Adaptive FEC
Kalamazoo, June 13, 2012
> 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
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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
Kalamazoo, June 13, 2012
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)
Kalamazoo, June 13, 2012
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)
Kalamazoo, June 13, 2012
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)
Kalamazoo, June 13, 2012
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Channel Activity Monitoring
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> 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
Kalamazoo, June 13, 2012
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Overall Comparison of Throughput
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Overall Comparison of Energy Consumption
Kalamazoo, June 13, 2012
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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
Kalamazoo, June 13, 2012
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Thanks for your attention !
> [email protected] > http://cds.unibe.ch
Kalamazoo, June 13, 2012