Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/9014
Title: A Payoff-Based Learning Approach to Cooperative Environmental Monitoring for PTZ Visual Sensor Networks
Authors: Hatanaka, Takeshi 
Wasa, Yasuaki 
Funada, Riku 
Charalambides, Alexandros G. 
Fujita, Masayuki 
Keywords: Cyber-physical systems
Environmental monitoring
Game theoretic cooperative control
Payoff-based learning
Visual sensor networks
Issue Date: 1-Mar-2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: IEEE Transactions on Automatic Control, 2016, Volume 61, Issue 3, Article number 7138601, Pages 709-724
Abstract: This paper addresses cooperative environmental monitoring for Pan-Tilt-Zoom (PTZ) visual sensor networks. In particular, we investigate the optimal monitoring problem whose objective function value is intertwined with the uncertain state of the physical world. In addition, due to the large volume of vision data, it is desired for each sensor to execute processing through local computation and communication. To address these issues, we present a distributed solution to the problem based on game theoretic cooperative control and payoff-based learning. At the first stage, a utility function is designed so that the resulting game constitutes a potential game with potential function equal to the group objective function, where the designed utility is shown to be computable through local image processing and communication. Then, we present a payoff-based learning algorithm so that the sensors are led to the global objective function maximizers without using any prior information on the environmental state. Finally, we run experiments to demonstrate the effectiveness of the present approach.
URI: http://ktisis.cut.ac.cy/handle/10488/9014
ISSN: 00189286
Rights: © 2015 IEEE
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