Analytical Study of Collaborative Information Coverage for Object Detection in Sensor Networks

Many sensor networks are deployed for the purpose of covering and monitoring a particular region, and detecting the object of interest in the region. In this paper, based on the probabilistic sensing model, we conduct comprehensive analytical and simulation studies on collaborative information coverage and object detection in wireless sensor networks. More specifically, we first define point information coverage and based on that we define p-coverage as a measure of the coverage performance for a randomly-deployed wireless sensor network. Then, we investigate the coverage and object detection performances of several simple decision fusion-based collaborative mechanisms and find that simple collaborations among active sensors indeed degrade the coverage performance due to the requirement of maintaining the target false detection probability. This motivates us to develop an on-demand collaborative framework for object detection, whose effectiveness is supported by detailed theoretical analysis and simulation-based validation. Finally, we investigate the energy efficiency performance of our proposed framework and identify the trade-offs among various system parameters in network power consumption.