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Approximate Dynamic Programming for Communication-Constrained Sensor Network Management

Williams, J.L. ; Fisher, J.W. ; Willsky, A.S.

IEEE transactions on signal processing, 2007-08, Vol.55 (8), p.4300-4311 [Periódico revisado por pares]

New York, NY: IEEE

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Título:
Approximate Dynamic Programming for Communication-Constrained Sensor Network Management
Autor: Williams, J.L. ; Fisher, J.W. ; Willsky, A.S.
Assuntos: Adaptive estimation ; Applied sciences ; Approximation ; Costs ; Density measurement ; Detection, estimation, filtering, equalization, prediction ; Dynamic programming ; Energy measurement ; Exact sciences and technology ; Information, signal and communications theory ; Intelligent sensors ; Kinematics ; Laboratories ; Mathematical models ; Networks ; Operations research ; Pattern recognition ; Probability density function ; Probability density functions ; Resource management ; Sensor phenomena and characterization ; Sensors ; Signal and communications theory ; Signal processing ; Signal, noise ; Studies ; Telecommunications and information theory ; tracking ; Transmission
É parte de: IEEE transactions on signal processing, 2007-08, Vol.55 (8), p.4300-4311
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
Descrição: Resource management in distributed sensor networks is a challenging problem. This can be attributed to the fundamental tradeoff between the value of information contained in a distributed set of measurements versus the energy costs of acquiring measurements, fusing them into the conditional probability density function (pdf) and transmitting the updated conditional pdf. Communications is commonly the highest contributor among these costs, typically by orders of magnitude. Failure to consider this tradeoff can significantly reduce the operational lifetime of a sensor network. While a variety of methods have been proposed that treat a subset of these issues, the approaches are indirect and usually consider at most a single time step. In the context of object tracking with a distributed sensor network, we propose an approximate dynamic programming approach that integrates the value of information and the cost of transmitting data over a rolling time horizon. We formulate this tradeoff as a dynamic program and use an approximation based on a linearization of the sensor model about a nominal trajectory to simultaneously find a tractable solution to the leader node selection problem and the sensor subset selection problem. Simulation results demonstrate that the resulting algorithm can provide similar estimation performance to that of the common most informative sensor selection method for a fraction of the communication cost.
Editor: New York, NY: IEEE
Idioma: Inglês

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Approximate Dynamic Programming for Communication-Constrained Sensor Network Management

Williams, J.L. ; Fisher, J.W. ; Willsky, A.S.

New York, NY: IEEE

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