InforLorV4, PascalFrancis, Curation, bibRecord, 000873

Spatio-temporal modelling-based drift-aware wireless sensor networks

Identifieur interne : 000873 ( PascalFrancis/Curation ); précédent : 000872; suivant : 000874

Spatio-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.

RBID : Pascal:11-0386595

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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C03	`09`	`X`	`ENG`	`@0 Cross correlation @5 09`
C03	`09`	`X`	`SPA`	`@0 Correlación cruzada @5 09`
C03	`10`	`X`	`FRE`	`@0 Machine vecteur support @5 10`
C03	`10`	`X`	`ENG`	`@0 Support vector machine @5 10`
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C03	`14`	`X`	`ENG`	`@0 Recursive method @5 14`
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C03	`16`	`X`	`ENG`	`@0 Random error @5 16`
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C03	`17`	`X`	`FRE`	`@0 filtre kalman UKF @5 17`
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Le document en format XML

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<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>
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<s5>01</s5>
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<fC03 i1="02" i2="X" l="FRE"><s0>Télédétection</s0>
<s5>02</s5>
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<fC03 i1="02" i2="X" l="ENG"><s0>Remote sensing</s0>
<s5>02</s5>
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<fC03 i1="02" i2="X" l="SPA"><s0>Teledetección</s0>
<s5>02</s5>
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<fC03 i1="03" i2="X" l="FRE"><s0>Réseau capteur</s0>
<s5>03</s5>
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<s5>03</s5>
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<s5>03</s5>
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<s5>04</s5>
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<fC03 i1="14" i2="X" l="FRE"><s0>Méthode récursive</s0>
<s5>14</s5>
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<s5>14</s5>
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<s5>15</s5>
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<s5>15</s5>
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<s5>16</s5>
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<s5>16</s5>
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<s5>16</s5>
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<fC03 i1="17" i2="X" l="FRE"><s0>filtre kalman UKF</s0>
<s5>17</s5>
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<fC03 i1="17" i2="X" l="ENG"><s0>Unscented Kalman filter</s0>
<s5>17</s5>
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<fC03 i1="17" i2="X" l="SPA"><s0>Filtro UKF</s0>
<s5>17</s5>
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<fC03 i1="18" i2="X" l="FRE"><s0>Linéarité</s0>
<s5>18</s5>
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<fC03 i1="18" i2="X" l="ENG"><s0>Linearity</s0>
<s5>18</s5>
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<fC03 i1="18" i2="X" l="SPA"><s0>Linearidad</s0>
<s5>18</s5>
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<fC03 i1="19" i2="X" l="FRE"><s0>Détection signal</s0>
<s5>46</s5>
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<s5>46</s5>
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<fC03 i1="19" i2="X" l="SPA"><s0>Detección señal</s0>
<s5>46</s5>
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<fC03 i1="20" i2="X" l="FRE"><s0>Télécommunication sans fil</s0>
<s5>47</s5>
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<fC03 i1="20" i2="X" l="ENG"><s0>Wireless telecommunication</s0>
<s5>47</s5>
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<fC03 i1="20" i2="X" l="SPA"><s0>Telecomunicación sin hilo</s0>
<s5>47</s5>
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<fC03 i1="21" i2="3" l="FRE"><s0>Classification signal</s0>
<s5>48</s5>
</fC03>
<fC03 i1="21" i2="3" l="ENG"><s0>Signal classification</s0>
<s5>48</s5>
</fC03>
<fC03 i1="22" i2="X" l="FRE"><s0>Classification automatique</s0>
<s5>49</s5>
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<fC03 i1="22" i2="X" l="ENG"><s0>Automatic 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>
</record>

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   |texte=   Spatio-temporal modelling-based drift-aware wireless sensor networks
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Spatio-temporal modelling-based drift-aware wireless sensor networks

Spatio-temporal modelling-based drift-aware wireless sensor networks

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