Energy Efficient Target Tracking in Wireless Sensor Networks: Sleep Scheduling, Particle Filtering, and Constrained Flooding

(ABSTRACT) Energy efficiency is a critical feature of wireless sensor networks (WSNs), because sensor nodes run on batteries that are generally difficult to recharge once deployed. For target tracking—one of the most important WSN application types—energy efficiency needs to be considered in various forms and shapes, such as idle listening, trajectory estimation, and data propagation. In this dissertation, we study three correlated problems on energy efficient target tracking in WSNs: sleep scheduling, particle filtering, and constrained flooding. We develop a Target Prediction and Sleep Scheduling protocol (TPSS) to improve energy efficiency for idle listening. We start with designing a target prediction method based on both kinematics and probability. Based on target prediction and proactive wake-up, TPSS precisely selects the nodes to awaken and reduces their active time, so as to enhance energy efficiency with limited tracking performance loss. In addition, we expand Sleep Scheduling to Multiple Target Tracking (SSMTT), and further reduce the energy consumption by leverag-ing the redundant alarm messages of interfering targets. Our simulation-based experimental studies show that compared to existing protocols such as Circle scheme and MCTA, TPSS and SSMTT introduce an improvement of 25% ∼ 45% on energy efficiency, at the expense of only 5% ∼ 15% increase on the detection delay. Particle Filtering is one of the most widely used Bayesian estimation methods, when target tracking is considered as a dynamic state estimation problem for trajectory estimation. However , the significant computational and communication complexity prohibits its application in WSNs. We design two particle filters (PFs)—Vector space based Particle Filter (VPF) and Completely Distributed Particle Filter (CDPF)—to improve energy efficiency of PFs by reducing the number of particles and the communication cost. Our experimental evaluations show that even though VPF incurs 34% more estimation error than RPF, and CDPF incurs a similar estimation error to SDPF, they significantly improve the energy efficiency by as much as 68% and 90% respectively. For data propagation, we present a Constrained Flooding protocol (CFlood) to enhance energy efficiency by increasing the deadline satisfaction ratio per unit energy consumption of time-sensitive packets. CFlood improves real-time performance by flooding, but effectively constrains energy consumption by controlling the scale of flooding—i.e., flooding only when necessary. If unicasting meets the distributed sub-deadline at a hop, CFlood aborts further flooding even after flooding has occurred in the current hop. Our simulation-based experimental studies show that CFlood achieves higher deadline satisfaction ratio per unit energy …