  This paper investigates cooperative environmental monitoring for
Pan-Tilt-Zoom (PTZ) visual sensor networks. We first present a novel
formulation of the optimal environmental monitoring problem, whose objective
function is intertwined with the uncertain state of the environment. 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
the 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.
