An Energy-Efficient Tracking Algorithm Based on Gene Expression Programming in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are widely used in detecting, locating and tracking moving objects. The cheap, low-powered and energy-limited sensors that are set up in large areas may consume large portion of energy and disable the whole network. In this paper, a new energy-efficient method based on Distributed Incremental Gene Expression Programming is proposed to discover the moving patterns of moving objects in order to turn on/off some sensor nodes at certain time to save energy. The main contributions include: a) Distributed GEP methods are used to perform collaborative mining the patterns of moving objects, b) adjustable sliding window are adopted to balance the trade-off of the high accuracy and low energy consumption, c) simulation results show that the proposed GEP-based motion prediction algorithm can greatly improve the tracking efficiency, increase the lifetime of the network by around 25% compared to other tracking algorithms, i.e., EKF and ECPA