A Payoff-Based Learning Approach to Cooperative Environmental Monitoring for PTZ Visual Sensor Networks

Takeshi Hatanaka, Yasuaki Wasa, Riku Funada, Alexandros G. Charalambides, Masayuki Fujita

研究成果: Article査読

10 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号7138601
ページ(範囲)709-724
ページ数16
ジャーナルIEEE Transactions on Automatic Control
61
3
DOI
出版ステータスPublished - 2016 3 1
外部発表はい

ASJC Scopus subject areas

  • 制御およびシステム工学
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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