Spatio-temporal modelling-based drift-aware wireless sensor networks
Identifieur interne : 000119 ( PascalFrancis/Checkpoint ); précédent : 000118; suivant : 000120Spatio-temporal modelling-based drift-aware wireless sensor networks
Auteurs : M. Takruri [Australie] ; S. Rajasegarar [Australie] ; S. Challa [Australie] ; C. Leckie [Australie] ; M. Palaniswami [Australie]Source :
- IET wireless sensor systems : (Print) [ 2043-6386 ] ; 2011.
Descripteurs français
- Pascal (Inist)
- Modélisation, Télédétection, Réseau capteur, Réseau sans fil, Monitorage, Algorithme, Erreur mesure, Corrélation temporelle, Corrélation croisée, Machine vecteur support, Analyse régression, Apprentissage, Mesure phase, Méthode récursive, Système décentralisé, Erreur aléatoire, filtre kalman UKF, Linéarité, Détection signal, Télécommunication sans fil, Classification signal, Classification automatique.
- Wicri :
- topic : Télédétection, Télécommunication sans fil.
English descriptors
- KwdEn :
- Algorithm, Automatic classification, Cross correlation, Decentralized system, Learning, Linearity, Measurement error, Modeling, Monitoring, Phase measurement, Random error, Recursive method, Regression analysis, Remote sensing, Sensor array, Signal classification, Signal detection, Support vector machine, Time correlation, Unscented Kalman filter, Wireless network, Wireless telecommunication.
Abstract
Wireless sensor networks are deployed for the purpose of monitoring an area of interest. Even when the sensors are properly calibrated at the time of deployment, they develop drift in their readings leading to erroneous network inferences. Based on the assumption that neighbouring sensors have correlated measurements and that the instantiations of drifts in sensors are uncorrelated, the authors present a novel algorithm for detecting and correcting sensor measurement errors. The authors use statistical modelling rather than physical relations to model the spatio-temporal cross-correlations among sensors. This in principle makes the framework presented applicable to most sensing problems. Each sensor in the network trains a support vector regression algorithm on its neighbours' corrected readings to obtain a predicted value for its future measurements. This phase is referred to here as the training phase. In the running phase, the predicted measurements are used by each node, in a recursive decentralised fashion, to self-assess its measurement and to detect and correct its drift and random error using an unscented Kalman filter. No assumptions regarding the linearity of drift or the density (closeness) of sensor deployment are made. The authors also demonstrate using real data obtained from the Intel Berkeley Research Laboratory that the proposed algorithm successfully suppresses drifts developed in sensors and thereby prolongs the effective lifetime of the network.
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Takruri</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>Centre for Real-Time Information Networks (CRIN), University of Technology</s1><s2>Sydney</s2><s3>AUS</s3><sZ>1 aut.</sZ></inist:fA14><country>Australie</country><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName></affiliation></author><author><name sortKey="Rajasegarar, S" sort="Rajasegarar, S" uniqKey="Rajasegarar S" first="S." last="Rajasegarar">S. Rajasegarar</name><affiliation wicri:level="4"><inist:fA14 i1="02"><s1>Department of Electrical and Electronic Engineering, The University of Melbourne</s1><s2>Melbourne</s2><s3>AUS</s3><sZ>2 aut.</sZ><sZ>5 aut.</sZ></inist:fA14><country>Australie</country><placeName><settlement type="city">Melbourne</settlement><region type="état">Victoria (État)</region></placeName><orgName type="university">Université de Melbourne</orgName></affiliation></author><author><name sortKey="Challa, S" sort="Challa, S" uniqKey="Challa S" first="S." last="Challa">S. Challa</name><affiliation wicri:level="4"><inist:fA14 i1="03"><s1>NICTA Victoria Research Laboratory, The University of Melbourne</s1><s2>Melbourne</s2><s3>AUS</s3><sZ>3 aut.</sZ></inist:fA14><country>Australie</country><placeName><settlement type="city">Melbourne</settlement><region type="état">Victoria (État)</region></placeName><orgName type="university">Université de Melbourne</orgName></affiliation></author><author><name sortKey="Leckie, C" sort="Leckie, C" uniqKey="Leckie C" first="C." last="Leckie">C. Leckie</name><affiliation wicri:level="4"><inist:fA14 i1="04"><s1>NICTA Victoria Research Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne</s1><s2>Melbourne</s2><s3>AUS</s3><sZ>4 aut.</sZ></inist:fA14><country>Australie</country><placeName><settlement type="city">Melbourne</settlement><region type="état">Victoria (État)</region></placeName><orgName type="university">Université de Melbourne</orgName></affiliation></author><author><name sortKey="Palaniswami, M" sort="Palaniswami, M" uniqKey="Palaniswami M" first="M." last="Palaniswami">M. Palaniswami</name><affiliation wicri:level="4"><inist:fA14 i1="02"><s1>Department of Electrical and Electronic Engineering, The University of Melbourne</s1><s2>Melbourne</s2><s3>AUS</s3><sZ>2 aut.</sZ><sZ>5 aut.</sZ></inist:fA14><country>Australie</country><placeName><settlement type="city">Melbourne</settlement><region type="état">Victoria (État)</region></placeName><orgName type="university">Université de Melbourne</orgName></affiliation></author></analytic><series><title level="j" type="main">IET wireless sensor systems : (Print)</title><title level="j" type="abbreviated">IET wirel. sens. syst. : (Print)</title><idno type="ISSN">2043-6386</idno><imprint><date when="2011">2011</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">IET wireless sensor systems : (Print)</title><title level="j" type="abbreviated">IET wirel. sens. syst. : (Print)</title><idno type="ISSN">2043-6386</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithm</term><term>Automatic classification</term><term>Cross correlation</term><term>Decentralized system</term><term>Learning</term><term>Linearity</term><term>Measurement error</term><term>Modeling</term><term>Monitoring</term><term>Phase measurement</term><term>Random error</term><term>Recursive method</term><term>Regression analysis</term><term>Remote sensing</term><term>Sensor array</term><term>Signal classification</term><term>Signal detection</term><term>Support vector machine</term><term>Time correlation</term><term>Unscented Kalman filter</term><term>Wireless network</term><term>Wireless telecommunication</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Modélisation</term><term>Télédétection</term><term>Réseau capteur</term><term>Réseau sans fil</term><term>Monitorage</term><term>Algorithme</term><term>Erreur mesure</term><term>Corrélation temporelle</term><term>Corrélation croisée</term><term>Machine vecteur support</term><term>Analyse régression</term><term>Apprentissage</term><term>Mesure phase</term><term>Méthode récursive</term><term>Système décentralisé</term><term>Erreur aléatoire</term><term>filtre kalman UKF</term><term>Linéarité</term><term>Détection signal</term><term>Télécommunication sans fil</term><term>Classification signal</term><term>Classification automatique</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Télédétection</term><term>Télécommunication sans fil</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Wireless sensor networks are deployed for the purpose of monitoring an area of interest. Even when the sensors are properly calibrated at the time of deployment, they develop drift in their readings leading to erroneous network inferences. Based on the assumption that neighbouring sensors have correlated measurements and that the instantiations of drifts in sensors are uncorrelated, the authors present a novel algorithm for detecting and correcting sensor measurement errors. The authors use statistical modelling rather than physical relations to model the spatio-temporal cross-correlations among sensors. This in principle makes the framework presented applicable to most sensing problems. Each sensor in the network trains a support vector regression algorithm on its neighbours' corrected readings to obtain a predicted value for its future measurements. This phase is referred to here as the training phase. In the running phase, the predicted measurements are used by each node, in a recursive decentralised fashion, to self-assess its measurement and to detect and correct its drift and random error using an unscented Kalman filter. No assumptions regarding the linearity of drift or the density (closeness) of sensor deployment are made. The authors also demonstrate using real data obtained from the Intel Berkeley Research Laboratory that the proposed algorithm successfully suppresses drifts developed in sensors and thereby prolongs the effective lifetime of the network.</div></front></TEI><inist><standard h6="B"><pA><fA01 i1="01" i2="1"><s0>2043-6386</s0></fA01><fA03 i2="1"><s0>IET wirel. sens. syst. : (Print)</s0></fA03><fA05><s2>1</s2></fA05><fA06><s2>2</s2></fA06><fA08 i1="01" i2="1" l="ENG"><s1>Spatio-temporal modelling-based drift-aware wireless sensor networks</s1></fA08><fA11 i1="01" i2="1"><s1>TAKRURI (M.)</s1></fA11><fA11 i1="02" i2="1"><s1>RAJASEGARAR (S.)</s1></fA11><fA11 i1="03" i2="1"><s1>CHALLA (S.)</s1></fA11><fA11 i1="04" i2="1"><s1>LECKIE (C.)</s1></fA11><fA11 i1="05" i2="1"><s1>PALANISWAMI (M.)</s1></fA11><fA14 i1="01"><s1>Centre for Real-Time Information Networks (CRIN), University of Technology</s1><s2>Sydney</s2><s3>AUS</s3><sZ>1 aut.</sZ></fA14><fA14 i1="02"><s1>Department of Electrical and Electronic Engineering, The University of Melbourne</s1><s2>Melbourne</s2><s3>AUS</s3><sZ>2 aut.</sZ><sZ>5 aut.</sZ></fA14><fA14 i1="03"><s1>NICTA Victoria Research Laboratory, The University of Melbourne</s1><s2>Melbourne</s2><s3>AUS</s3><sZ>3 aut.</sZ></fA14><fA14 i1="04"><s1>NICTA Victoria Research Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne</s1><s2>Melbourne</s2><s3>AUS</s3><sZ>4 aut.</sZ></fA14><fA20><s1>110-122</s1></fA20><fA21><s1>2011</s1></fA21><fA23 i1="01"><s0>ENG</s0></fA23><fA43 i1="01"><s1>INIST</s1><s2>7573U</s2><s5>354000191149750070</s5></fA43><fA44><s0>0000</s0><s1>© 2011 INIST-CNRS. All rights reserved.</s1></fA44><fA45><s0>53 ref.</s0></fA45><fA47 i1="01" i2="1"><s0>11-0386595</s0></fA47><fA60><s1>P</s1></fA60><fA61><s0>A</s0></fA61><fA64 i1="01" i2="1"><s0>IET wireless sensor systems : (Print)</s0></fA64><fA66 i1="01"><s0>GBR</s0></fA66><fC01 i1="01" l="ENG"><s0>Wireless sensor networks are deployed for the purpose of monitoring an area of interest. Even when the sensors are properly calibrated at the time of deployment, they develop drift in their readings leading to erroneous network inferences. Based on the assumption that neighbouring sensors have correlated measurements and that the instantiations of drifts in sensors are uncorrelated, the authors present a novel algorithm for detecting and correcting sensor measurement errors. The authors use statistical modelling rather than physical relations to model the spatio-temporal cross-correlations among sensors. This in principle makes the framework presented applicable to most sensing problems. Each sensor in the network trains a support vector regression algorithm on its neighbours' corrected readings to obtain a predicted value for its future measurements. This phase is referred to here as the training phase. In the running phase, the predicted measurements are used by each node, in a recursive decentralised fashion, to self-assess its measurement and to detect and correct its drift and random error using an unscented Kalman filter. No assumptions regarding the linearity of drift or the density (closeness) of sensor deployment are made. The authors also demonstrate using real data obtained from the Intel Berkeley Research Laboratory that the proposed algorithm successfully suppresses drifts developed in sensors and thereby prolongs the effective lifetime of the network.</s0></fC01><fC02 i1="01" i2="X"><s0>001D04B02H4</s0></fC02><fC02 i1="02" i2="X"><s0>001D04A04A2</s0></fC02><fC02 i1="03" i2="X"><s0>001D04A04A1</s0></fC02><fC03 i1="01" i2="X" l="FRE"><s0>Modélisation</s0><s5>01</s5></fC03><fC03 i1="01" i2="X" l="ENG"><s0>Modeling</s0><s5>01</s5></fC03><fC03 i1="01" i2="X" l="SPA"><s0>Modelización</s0><s5>01</s5></fC03><fC03 i1="02" i2="X" l="FRE"><s0>Télédétection</s0><s5>02</s5></fC03><fC03 i1="02" i2="X" l="ENG"><s0>Remote sensing</s0><s5>02</s5></fC03><fC03 i1="02" i2="X" l="SPA"><s0>Teledetección</s0><s5>02</s5></fC03><fC03 i1="03" i2="X" l="FRE"><s0>Réseau capteur</s0><s5>03</s5></fC03><fC03 i1="03" i2="X" l="ENG"><s0>Sensor array</s0><s5>03</s5></fC03><fC03 i1="03" i2="X" l="SPA"><s0>Red 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classification</s0><s5>49</s5></fC03><fC03 i1="22" i2="X" l="SPA"><s0>Clasificación automática</s0><s5>49</s5></fC03><fN21><s1>262</s1></fN21><fN44 i1="01"><s1>OTO</s1></fN44><fN82><s1>OTO</s1></fN82></pA></standard></inist><affiliations><list><country><li>Australie</li></country><region><li>Nouvelle-Galles du Sud</li><li>Victoria (État)</li></region><settlement><li>Melbourne</li><li>Sydney</li></settlement><orgName><li>Université de Melbourne</li></orgName></list><tree><country name="Australie"><region name="Nouvelle-Galles du Sud"><name sortKey="Takruri, M" sort="Takruri, M" uniqKey="Takruri M" first="M." last="Takruri">M. Takruri</name></region><name sortKey="Challa, S" sort="Challa, S" uniqKey="Challa S" first="S." last="Challa">S. Challa</name><name sortKey="Leckie, C" sort="Leckie, C" uniqKey="Leckie C" first="C." last="Leckie">C. Leckie</name><name sortKey="Palaniswami, M" sort="Palaniswami, M" uniqKey="Palaniswami M" first="M." last="Palaniswami">M. Palaniswami</name><name sortKey="Rajasegarar, S" sort="Rajasegarar, S" uniqKey="Rajasegarar S" first="S." last="Rajasegarar">S. Rajasegarar</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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