Development and Evaluation of Energy-Efficient and Adaptive Protocolsfor Wireless Sensor Networks

IEEE 3rd Latin-American Conference on
Communications – LATINCOM 2011

Development and Evaluation of
Energy-Efficient and Adaptive Protocols
for Wireless Sensor Networks

Torsten Braun, Universität Bern, Switzerland
braun@iam.unibe.ch, rvs.unibe.ch

joint work with many other persons, see publications

Torsten Braun: Development and Evaluation of Energy-Efficient and Adaptive Protocols for 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
> Conclusions
> Outlook: Mobility Support

Belém, October 24, 2011 2


Wireless Sensor Network Applications

> REDE (REde de sensores sem fio
para Detectar Enchentes) project
(USP São Carlos,
http://sp-river.blogspot.com)
> Outdoor deployment of wireless
sensor network to measure water
depth and pollution in creeks of
the city of São Carlos SP
> SunSPOT motes and hydrostatic
level and conductivity sensors.
Jó Ueyama, Daniel Roy Hughes, Nelson Matthys,
Wouter Horré, Wouter Joosen, Christophe Huygens,
Sam Michiels:
An Event-based Component Model for Wireless
Sensor Networks: a Case Study for River Monitoring,
XXVIII Simpósio Brasileiro de Redes de
Computadores e Sistemas Distribuídos




> Environmental monitoring (A4-Mesh, a4-mesh.unibe.ch)
Thomas Staub, Benjamin
Nyffenegger, Desislava Dimitrova,
Torsten Braun:
Operational Support of Wireless
Mesh Networks Deployed for
Extending Network Connectivity,
1st International Workshop on
Opportunistic Sensing and
Processing in Mobile Wireless
Sensor and Cellular networks
(MobiSense), Bilbao, May 2011



A4-Mesh Impressions




> 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



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


Wireless Sensor Network Testbed
(WISEBED)
> Recently finished FP7 Future Internet
research project
> wisebed.eu
> Pan-European federation of
9 WSN testbeds Testbed
Testbed
Testbed
> approx. 1000 deployed nodes
> Each partner runs own testbed
with different hardware. Testbed

> Use of individual testbed or
interconnected testbeds Testbed

Testbed



General Testbed Infrastructure

> Portal WSN
Testbed
— Gateway between Internet and WSN part of
Internet WISEBED
— Used for WSN management
> Wireless Sensor Network
— Sensor nodes communicate with
each other. Portal

— Backbone used to communicate with DB

portal. WSN
Backbone Testbed
> Internet part of
WISEBED
— connects all WISEBED testbeds Wireless
Sensor Network
(WSN)



Testbed @ Universität Bern

Mesh Node

Internet
USB
Ethernet
LAN
wireless
Portal
(running TARWIS
management system)

Sensor Node

Belém, October 24, 2011
12


Testbed @ Universität Bern

> Approx. 50 TelosB/MSB430 nodes connected to
portal via Ethernet with temperature, humidity, light
sensors



TARWIS System Architecture



Testbed / Sensor Node Reservation

> Reservation system maintains per-site reservation database.
> User Interfaces
— Web-based user interface
— iPhone application



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 in order
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


MAC Protocols for Wireless Sensor Networks

1. Scheduled Protocols
— Multiplexing and Allocation of channels, e.g., time multiplexing,
requires accurate time synchronization

2. Contention-based Protocols
— Channel sharing and allocation on-demand, often: periodic wake-ups
— Problems: collisions and delays
— Sender must ensure that receiver is awake during transmission
– Transmissions of long preambles/beacons (B-MAC, X-MAC, WiseMAC)
– Weakly synchronized wakeups (S-MAC, T-MAC)
– Receiver signals wakeup (RI-MAC)
— Load adaptation
– Adaptation of wakeup time dependent on activation events or load
(T-MAC, X-MAC)

Wake-up interval


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
— Max. clock drift: 2θL, sender must start preamble transmission 2θL
prior to wakeup and transmit it until 2θL after wakeup.
> „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
> integrates destination address into preamble to reduce overhearing
> 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



Implementation Experiences

Implementation of MaxMAC on MSB430 using Scatterweb
operating system raised several problems:
> Inaccurate execution of timers,
e.g., because of active event processing at timer expiration
→ somewhat earlier scheduling of timers
> Overhearing avoidance has not been supported by node
hardware.



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 Barhlomé,
> Stateful Sender Receiver Adaptive (SSRA) 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



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
— Several experiences (testbeds, protocols) to be applied in other
areas, e.g. wireless mesh and ad-hoc networks
— Mobility support in wireless sensor / mesh network testbeds



Wireless Sensor Networks and UAVs in
Agriculture

Field not to be sprayed

Sensor-UAV-link

UAV-UAV-link Fausto Guzzo da Costa, Torsten
Braun, Jó Ueyama, Gustavo Pessin,
Fernando Santos:Arquitetura baseada
chemicals em veículos aéreos não tripulados e
redes de sensores sem fio para
Field to be sprayed aplicações agrícolas, VIII Congresso
Brasileiro de Agroinformatica,
Belém, October 24, 2011 SBIAGRO 2011, Bento Gonçalves 43


VirtualMesh:
Wireless and Mobile Network Emulation

Thomas Staub, Reto Gantenbein, Torsten Braun:
VirtualMesh: an emulation framework for wireless
mesh and ad hoc networks in OMNeT++,
SIMULATION: Transaction of the Society for
Modelling and Simulation International, Vol. 87,
No. 1-2, January 1, 2011



Mobility Support in Wisebed by VirtualMesh

Torsten Braun, Geoff Coulson, Thomas Staub: Towards Virtual Mobility Support in a Federated Testbed for Wireless Sensor
Networks, 6th Workshop on Wireless and Mobile Ad-Hoc Networks (WMAN 2011), Kiel, Germany, March 10 - 11, 2011



Thanks for your attention !

> Contact: braun@iam.unibe.ch
> More information: rvs.unibe.ch


Development and Evaluation of Energy-Efficient and Adaptive Protocols for Wireless Sensor Networks

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