Probabilistic coverage based sensor scheduling for target tracking sensor networks

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, a–k-coverage, which can guarantee that target moving area is covered by at least k sensors under at least a probability. Then, we propose an energy-efficient sensor scheduling scheme, Optimal Cooperation Scheduling Algorithm (OCSA), to balance tracking quality and network lifetime under a–k-coverage condition. The effectiveness of the proposed scheme is validated through extensive simulation experiments. Wireless sensor networks (WSNs) are an emerging technology that has many applications. These networks are composed of hundreds, and potentially thousands of tiny sensor nodes, functioning autonomously, and serving different purposes. They are typically battery powered with limited communication and computation abilities. Each node is equipped with a variety of sensing modalities, such as acoustic, seismic, and infrared. Applications of WSNs include battlefield surveillance, environment monitoring, biological detection, home appliance and inventory tracking. In this paper, we focus on target tracking application. Target tracking is an important application of wireless sensor networks, such as vehicle tracking in military surveillance [32] and wild animal tracking in habitat monitoring [7,20]. In these applications, tracking quality and network lifetime are two conflicting requirements due to the limited battery power of the sensor nodes. With unlimited power supply, a given area can be monitored perfectly with a set of sensor nodes that cover the entire area in terms of sensing. However, since the sensor nodes have limited power, the quality of monitoring becomes inversely proportional to the life time of the network. Thus, 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 [11,38]. It is a challenge task to design an energy-efficient tracking algorithm for target tracking application due to the limited battery power of the sensor node, which aims at increasing the network lifetime. Most works recently in target tracking application focus on the deployment phase of wireless sensor networks. How to schedule sensors dynamically and timely by taking into account of both the energy consumption and tracking quality is …