Algoritmos para rastreamento de alvos em áreas quantizadas com redes de sensores sem fio

Abstract

Target tracking in Wireless Sensor Networks (WSNs) is an application in which the nodes cooperate to estimate the position of one or more objects of interest. In this context, the contributions of this work are fourfold. First, a survey the state-of-the-art about target tracking algorithms, in which we identified three formulations of tracking problem, and we classified them according to their characteristics. Furthermore, we divided the target tracking process in components to make the general understanding easier. Second, we propose and evaluate the PRATIQUE algorithm for tracking animals in forests. In this case, the nodes are organized into a grid to make feasible the use of sensor nodes in this kind of area in such a way that each cell of the grid is a region that can be occupied by the target. The algorithm estimates the cell where the target is, and uses predictions and hybrid clustering to reduce the communication cost and ensure the tracking accuracy. The results of the simulations show that prediction errors are approximately one cell. The third contribution is the TATI algorithm, this algorithm guides a tracker to approach the target. The sensor network is organized into faces to make the cooperation among the nodes easier, and reduce the path between the tracker and the target. The results show that energy consumption is reduced by 15%, and the tracker stays about 10m closer to the target, compared to the baseline. The fourth contribution is a scheme for performing localization and tracking tasks simultaneously in such a way that errors of range-based localization algorithms are reduced. This algorithm takes advantage of the messages sent to track the target to filter the noise in the distance estimation, reducing localization errors while tracking. The results show that the localization errors can be reduced by up to 70%.