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, Switzerland
braun@iam.unibe.ch, cds.unibe.ch

joint work with Philipp Hurni

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

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 2


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



Wireless Sensor Network Applications

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

Tseuzier
storage lake

Sierre
Sion

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



Application Requirements

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



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


Wisebed WSN Testbed @ Universität Bern

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

USB Mesh Node Internet
LAN
wireless
Ethernet

Portal

Sensor Node



TARWIS Experiment Configuration



TARWIS Experiment Monitoring



TARIWS-Generated Experiment Trace


Software-Based Estimation of
Energy Consumption


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)



Hardware-Based Energy Measurements

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



Simple 3-State-Model

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



Measured vs. Estimated Energy Consumption

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



3-State-Model with State Transitions

Revised estimation:


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


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



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.

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

S1 S2
Base CSMA
2* 4*
state duty duty
RECV
cycle cycle

packet rate < T1 packet rate < T2 packet rate < TCSMA
Lease expired Lease expired Lease expired


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.



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



Experiments with Intruder Scenario I

WiseMAC

MaxMAC

CSMA



Experiments with Intruder Scenario II


Adaptive Forward Error Correction


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


Implementation of FEC Library

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



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
Philipp Hurni, Sebastian
> Stateful Sender Receiver Adaptive (SSRA) Barthlomé, Torsten Braun:
— Consideration of number of corrected bit errors Link-Quality Aware Run-Time
Adaptive Forward Error Correction
by receiver (to be reported in acknowledgement) Strategies in Wireless Sensor
Networks, submitted

(63,36)



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



Wisebed Experiments

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



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 for


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



Optimization of TCP in WSNs

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

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

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

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



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



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)



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


Long Idle Periods



Spatial Reuse by Multiple TCP Connections



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


Overall Comparison of Throughput



Overall Comparison of Energy Consumption



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



Thanks for your attention !

> braun@iam.unibe.ch
> http://cds.unibe.ch


Reliable and Energy-Efficient Communication in Wireless Sensor Networks

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (19)

Destaque

Destaque (17)

Semelhante a Reliable and Energy-Efficient Communication in Wireless Sensor Networks

Semelhante a Reliable and Energy-Efficient Communication in Wireless Sensor Networks (20)

Mais de Torsten Braun, Universität Bern

Mais de Torsten Braun, Universität Bern (20)

Último

Último (20)

Reliable and Energy-Efficient Communication in Wireless Sensor Networks