Abstract: ith continuous advances in sensor fabrication technology, the deployment of large-scale sensor networks for pervasive monitoring and control of physical systems has become increasingly feasible. Scalability, low overhead, and distributed operation are among the key requirements for protocols designed for such networks. In this paper, a Distributed Predictive Tracking (DPT) algorithm is proposed to address the problem of tracking mobile targets in wireless sensor networks. The proposed algorithm employs a cluster-based architecture to improve scalability and energy efficiency, while a lightweight prediction mechanism is used to estimate the future position of moving targets. Based on these predictions, only a limited set of sensors along the expected target trajectory are activated, allowing the remaining nodes to remain in a low-power state. The DPT algorithm is designed to be fully distributed and resilient to node and prediction failures, and it incorporates an efficient recovery mechanism to rapidly re-acquire lost targets with minimal additional energy consumption. Simulation results demonstrate that the proposed algorithm can accurately track targets with random movement patterns over a wide range of target speeds while maintaining low energy overhead.

Keywords: Sensor Networks, Mobile Targets, Tracking.

Downloads: | DOI: 10.17148/IJIREEICE.2025.131209