Decentralized Smart Sensor Scheduling for Multiple Target Tracking for Border Surveillance

Abstract— Border surveillance requires regular patrolling to prevent intruders from crossing across, emphasizing the need for an automated network of sensing devices that is capable of detecting and estimating multiple moving targets. This paper proposes a fusion-driven decentralized sensor scheduling scheme that enables dynamic space-time clustering around multiple moving targets for energy-efficient track estimation. Each sensor node runs a Probabilistic Finite State Automata (PFSA) that controls the sensing and communication devices in an energy-efficient manner. This decentralized scheduling scheme is validated and compared with traditional scheduling schemes. The results show that the proposed scheme conserves energy while maintaining accurate track estimation. Rapid advancements in sensing, communication and real-time data processing technologies have facilitated the development of Wireless Sensor Networks (WSN). WSN consist of multiple sensor nodes deployed throughout a Region Of Interest(ROI) to perform tasks. One example is border surveillance, which requires routine patrolling to prevent intruders from crossing across, thus emphasizing the need for an automated network that can detect and estimate multiple moving targets. < final year projects >