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This paper investigates cooperative environmental monitoring forPan-Tilt-Zoom (PTZ) visual sensor networks. We first present a novelformulation of the optimal environmental monitoring problem, whose objectivefunction is intertwined with the uncertain state of the environment. Inaddition, due to the large volume of vision data, it is desired for each sensorto execute processing through local computation and communication. To addressthe issues, we present a distributed solution to the problem based on gametheoretic cooperative control and payoff-based learning. At the first stage, autility function is designed so that the resulting game constitutes a potentialgame with potential function equal to the group objective function, where thedesigned utility is shown to be computable through local image processing andcommunication. Then, we present a payoff-based learning algorithm so that thesensors are led to the global objective function maximizers without using anyprior information on the environmental state. Finally, we run experiments todemonstrate the effectiveness of the present approach.

Cooperative Environmental Monitoring for PTZ Visual Sensor Networks: A Payoff-based Learning Approach