Εμφάνιση απλής εγγραφής

dc.creatorKoutsopoulos, I.en
dc.creatorHalkidi, M.en
dc.date.accessioned2015-11-23T10:36:45Z
dc.date.available2015-11-23T10:36:45Z
dc.date.issued2010
dc.identifier.isbn9781424475254
dc.identifier.urihttp://hdl.handle.net/11615/29967
dc.description.abstractWireless sensor networks are fundamentally different from other wireless networks due to energy constraints and spatial correlation among sensor measurements. Mechanisms that efficiently compress and transport sensor data in the network are needed. We consider the problem of maximizing lifetime of wireless sensor networks that are entitled with the task of estimating an unknown parameter or process and thus need to adhere to estimation error specifications. We investigate optimal endogenous sensor measurement rate control, in-network data aggregation and routing for achieving the goal above. Sensors take measurements and aggregate incoming data from neighbors in a single outgoing flow by applying appropriate aggregation weights. By doing so, they control the variance of outgoing flow. Each sensor controls its measurement rate and aggregation weights, and aggregated measurement data are routed to the FC for Maximum Likelihood (ML) estimation. The challenge is to find an optimal compromise between eliminating data redundancy and maintaining data representation accuracy so as to adhere to estimation quality constraints and reduce the volume of transported data, thus improving network lifetime. Sensor spatial correlation, measurement accuracies, link qualities and energy reserves affect sensor measurement rates, data aggregation and routes to the FC. On the other hand, measurement rates, aggregation, and sensor characteristics impact the estimation error. We show that the problem can be decomposed into separate optimization problems where each sensor autonomously takes its measurement rate, aggregation and routing decisions. We design an iterative primal-dual algorithm that relies on low overhead feedback from the FC to the nearest sensors, and on sensor neighbor Lagrange multiplier exchanges. Our work strikes the optimal fundamental tradeoff between network lifetime, in-network data aggregation and estimation quality and yields a solution based on distributed sensor coordination.en
dc.source.urihttp://www.scopus.com/inward/record.url?eid=2-s2.0-77955890348&partnerID=40&md5=d74cc6b8605fb6217861093a2c367358
dc.subjectAggregation weightsen
dc.subjectData aggregationen
dc.subjectData redundancyen
dc.subjectData representationsen
dc.subjectDistributed sensoren
dc.subjectEnergy constrainten
dc.subjectEnergy efficienten
dc.subjectEnergy reservesen
dc.subjectEstimation errorsen
dc.subjectEstimation qualityen
dc.subjectIn-network data aggregationen
dc.subjectLink qualityen
dc.subjectLow overheaden
dc.subjectMeasurement accuracyen
dc.subjectMeasurement dataen
dc.subjectNetwork lifetimeen
dc.subjectOptimization problemsen
dc.subjectPrimal dual algorithmsen
dc.subjectRate controlsen
dc.subjectRouting decisionsen
dc.subjectRouting techniquesen
dc.subjectSensor characteristicsen
dc.subjectSensor controlen
dc.subjectSensor dataen
dc.subjectSensor measurementsen
dc.subjectSpatial correlationsen
dc.subjectUnknown parametersen
dc.subjectAd hoc networksen
dc.subjectEstimationen
dc.subjectLagrange multipliersen
dc.subjectMaximum likelihood estimationen
dc.subjectMeasurementsen
dc.subjectOptimizationen
dc.subjectSensor networksen
dc.subjectWireless sensor networksen
dc.titleMeasurement aggregation and routing techniques for energy-efficient estimation in wireless sensor networksen
dc.typeconferenceItemen


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