Probabilistic coverage based sensor scheduling for target tracking sensor networks

Abstract

In target tracking sensor networks, tracking quality and network lifetime are two conflicting optimization goals due to the limited battery power of the sensor nodes. During the movement of a target, how to select an optimal subset of sensors to wake up is of critical importance both for extending network lifetime and guaranteeing tracking quality. In this paper, we first propose a probabilistic-based dynamic non-complete k-coverage method, α–k-coverage, which can guarantee that target moving area is covered by at least k sensors under at least α probability. Then, we propose an energy-efficient sensor scheduling scheme, Optimal Cooperation Scheduling Algorithm (OCSA), to balance tracking quality and network lifetime under α–k-coverage condition. The effectiveness of the proposed scheme is validated through extensive simulation experiments.