Game Theoretic Request Scheduling with Queue Priority in Video Sensor Networks

Abstract

Video sensor networks have been widely used to monitor environment and report abnormality. Each node collects video data, select a head node, and transmit the data to the head, and then the head reports the data to the base station. A head has to process both normal and abnormal data-reporting requests from its nearby nodes. To achieve QoS of surveillance, previous request scheduling methods minimize the data transmission delay or blocking rate but no comprehensive way was studied in the literature. In this paper, we propose a game strategic request scheduling based on a queue priority model in which a handover mechanism ensures that the abnormal requests are processed in time. In the game, video sensors select their heads to decide the arriving rates of both normal and abnormal requests; the heads decide the probability of handing over the abnormal requests. At the Nash Equilibrium Point (NEP), the normal data requesters optimize mean delay, the abnormal data requesters optimize mean blocking rate, and the heads balance the request load on them. Numerical analysis shows that the game strategic scheduling outperforms other scheduling methods that consider single objective (minimum delay or minimum blocking rate).