Energy-Efficient Predictive Tracking for Continuous Objects in Wireless Sensor Networks

Due to the severe resource constraints of sensor hardware, energy efficiency is one of critical factors for monitoring the movement of the large-scale phenomena such as wild fire and hazardous bio-chemical material, denoted by continuous objects. In order to save energy, most of existing research on tracking the continuous objects focuses on finding the ways to minimize the communication cost through the effective data delivery such as data aggregation and reducing the number of reporting nodes, and not much work has been done on sensor state scheduling. Energy efficiency is expected to improve if only sensor nodes near the boundary of continuous object actively participate in tracking process, while other sensor nodes stay on sleep state for energy saving. In this paper, we propose a predictive continuous object tracking scheme, called PRECO, which uses minimum set of active sensing nodes to reduce energy consumption. The proposed scheme predicts the future boundary line, which provides the knowledge for a wake-up mechanism to decide which sleeping nodes need to be activated for future tracking. The proposed algorithm is verified with simulation results that total energy consumption can be dramatically reduced under acceptable boundary detection accuracy.