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

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


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.

Original languageEnglish
Article number7138601
Pages (from-to)709-724
Number of pages16
JournalIEEE Transactions on Automatic Control
Issue number3
Publication statusPublished - 2016 Mar 1
Externally publishedYes


  • Cyber-physical systems
  • Environmental monitoring
  • Game theoretic cooperative control
  • Payoff-based learning
  • Visual sensor networks

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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