AHSWN Home · Issue Contents · Forthcoming Papers

Target Tracking in a Wireless Sensor Network with Meta-heuristic Optimization for Target Movement Prediction Scheme
P. Prabaharan, K. Radha, M. Meena and S. Jayachitra

Mobile Target Tracking is a crucial application in Wireless Sensor Networks (WSNs), notably for surveillance. The accuracy of tracking is heavily dependent on distance estimation or localization, and more research had already been accomplished in this area so far. This research suggests a novel energy-saving target tracking framework that is partitioned into two phases: (i) Mobility Target Tracking (TT) and (ii) Target Movement Prediction. Initially, the Extended Kalman Filter (EKF) is utilised to track the target. Following that, the target movement is predicted employing input factors including such Angle of Arrival (AoA) and Received Signal Strength (RSS), tends to result in the optimal movement of the mobile node. This circumstance is known as the optimization crisis whereas predicting optimal node movement is one of most complicated concerns in WSN. To improve the precision of the optimal prediction, a new hybrid algorithm termed HYBRID BINARY WHALE OPTIMIZATION ALGORITHM (HBWOA) is presented. Concerning Mean absolute error (MAE), the implemented model outperforms ELOT and AUKF by 0.45 and 0.3 %, respectively. Correspondingly, for noise variance 0.3, the proposed methodology outperformed ELOT and AUKF by 0.32 and 0.27 %. Using the Mean Squared Error (MSE) metric, the proposed model outperformed traditional kinds of frameworks ELOT and AUKF by 0.18 % and 0.07 % with the lowest error.

Keywords: Target Tracking, Target movement Prediction, AoA, RSS