Localization-oriented coverage based on Bayesian estimation in camera sensor networks

Target tracking and localization are important applications in camera sensor networks. Although coverage is a very important research topic in wireless sensor networks, and the coverage problem for target detection has been intensively studied, few considers the coverage problem from the perspective of target localization. In this paper, we investigate the coverage problem from the perspective of target localization for camera sensor networks. We first propose a novel localization-oriented sensing model based on the perspective projection of camera. Then, we propose a new notion of localization-oriented coverage (L-coverage for short). We assume that all camera sensors make the measurements independent of other in the field, and these camera sensors can cooperate to make an accurate estimation for the location of the target. In addition, the relationships among L-coverage, 2-coverage, and the density of camera sensors are also discussed in this paper. The obtained results show that our model can be effectively deployed in many practical scenarios.