In this paper, we propose and evaluate a distributed, energy-efficient, light-weight framework for target localization and tracking in wireless sensor networks. Since radio communication is the most energy-consuming operation, this framework aims to reduce the number of messages and the number of message collisions, while providing refined accuracy. The key element of the framework is a novel localization algorithm, called Ratiometric Vector Iteration (RVI). RVI is based on distance ratio estimates rather than absolute distance estimates which are often impossible to calculate. By iteratively updating the estimated location using the distance ratio, RVI localizes the target accurately with only three sensors' participation. After localization, the location of the target is reported to the subscriber. If the target is stationary or moves around within a small area, it is wasteful to report (almost) the same location estimates repeatedly. We, therefore, propose to dynamically adjust a reporting frequency considering the target's movement so that we can reduce the number of report messages while maintaining tracking quality. Extensive simulation results show that the proposed framework combining RVI and the movement-adaptive report scheduling algorithm reduces the localization error and total number of the transmitted messages up to half of those of the existing approaches.
['Jeongkeun Lee', 'Kideok Cho', 'Seung-Jae Lee', 'Taekyoung Kwon', 'Yanghee Choi']
Distributed and energy-efficient target localization and tracking in wireless sensor networks