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

作者：Hatanaka Takeshi^*; Wasa Yasuaki^*; Funada Riku^*; Charalambides Alexandros G^*; Fujita Masayuki^*

来源：IEEE Transactions on Automatic Control, 2016, 61(3): 709-724.

DOI：10.1109/TAC.2015.2450611

• Cyber-physical systems
• environmental monitoring
• game theoretic cooperative control
• payoff-based learning
• visual sensor networks

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.

• 出版日期2016-3