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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 6/2016

01.08.2016 | Original Article

ADP-based optimal sensor scheduling for target tracking in energy harvesting wireless sensor networks

verfasst von: Ruizhuo Song, Qinglai Wei, Wendong Xiao

Erschienen in: Neural Computing and Applications | Ausgabe 6/2016

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Abstract

This paper proposes a novel sensor scheduling scheme based on adaptive dynamic programming, which makes the sensor energy consumption and tracking error optimal over the system operational horizon for wireless sensor networks with solar energy harvesting. Neural network is used to model the solar energy harvesting. Kalman filter estimation technology is employed to predict the target location. A performance index function is established based on the energy consumption and tracking error. Critic network is developed to approximate the performance index function. The presented method is proven to be convergent. Numerical example shows the effectiveness of the proposed approach.
Vorheriger Artikel RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia
Nächster Artikel Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5

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Metadaten
Titel
ADP-based optimal sensor scheduling for target tracking in energy harvesting wireless sensor networks
verfasst von
Ruizhuo Song
Qinglai Wei
Wendong Xiao
Publikationsdatum
01.08.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 6/2016
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-1954-4

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