Game-Theoretic Camera Selection Using Inference Tree Method for a Wireless Visual Sensor Network

Abstract

In a wireless visual sensor network consisting of wireless, battery-powered, and field-of-view (FoV) overlapping and stationary visual sensors, trade-offs exist between extending network lifetime and enhancing its sensing accuracy. Moreover, aggregating individual inferences from each sensor is essential to generate a globally consistent inference, because these individual inferences can be biased by noise or other unexpected conditions. Those challenges can be addressed by reducing the amount of data transmission among the sensors and by activating, in a timely manner, only a desirable camera subset for given targets. In this paper, we initialize an optimal data transmission path among visual sensors using the inference tree method, which is vital for collecting individual inferences and building a global inference. Based on the optimal data transmission path, we model the camera selection problem in a cooperative bargaining game. In this game, based on the serial dictatorial rule, camera sensors cooperatively attempt to raise the overall sensing accuracy by sequentially deciding their own mode between “sleep” and “active” in descending order of their bargaining power. Simulated results demonstrate that our proposed approach outperforms other alternatives, resulting in reduced resource overhead and improved network lifetime and sensing accuracy.