Detecting Anonymous Target and Predicting Target Trajectories in Wireless Sensor Networks

Abstract

Target Tracking (TT) is an application of Wireless Sensor Networks (WSNs) which necessitates constant assessment of the location of a target. Any change in position of a target and the distance from each intermediate sensor node to the target is passed on to base station and these factors play a crucial role in further processing. The drawback of WSN is that it is prone to numerous constraints like low power, faulty sensors, environmental noises, etc. The target should be detected first and its path should be tracked continuously as it moves around the sensing region. This problem of detecting and tracking a target should be conducted with maximum accuracy and minimum energy consumption in each sensor node. In this paper, we propose a Target Detection and Target Tracking (TDTT) model for continuously tracking the target. This model uses prelocalization-based Kalman Filter (KF) for target detection and clique-based estimation for tracking the target trajectories. We evaluated our model by calculating the probability of detecting a target based on distance, then estimating the trajectory. We analyzed the maximum error in position estimation based on density and sensing radius of the sensors. The results were found to be encouraging. The proposed KF-based target detection and clique-based target tracking reduce overall expenditure of energy, thereby increasing network lifetime. This approach is also compared with Dynamic Object Tracking (DOT) and face-based tracking approach. The experimental results prove that employing TDTT improves energy efficiency and extends the lifetime of the network, without compromising the accuracy of tracking.

Mobility target tracking with meta‐heuristic aided target movement prediction scheme in WSN using adaptive distributed extended Kalman filtering

“…These two randomly selected positions are used for updating the final position. It is mathematically formulated in Equation (28).…”

“…This sensor node is called a static sensor node. The static sensor nodes in the WSN 28, 29 can be easily maintained, and the installation of these sensor nodes takes very little time. The location of the target ranges between AoA and RSS.…”

Energy-Efficient Object Detection and Tracking Framework for Wireless Sensor Network

“…P. Leela Rani and G. A. Sathish Kumar [ 7 ] proposed an energy-efficient object detection and tracking model named the TDTT model. The model uses the concept of cliques to cluster the nodes and a Kalman filter for object localization.…”