Improved Prediction Ratio using Target Movement Prediction Algorithm in Wireless Sensor Networks Navigation
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Thousands of self-organizing sensor nodes may collaborate and establish a distributed monitoring, Wireless Sensor Network (WSN) in the future. Of late, numerous WSN applications have been using GPS devices to track and locate the position of the remote sensor nodes. Due to expensive hardware resources and power constraints of the sensor nodes, the usage of GPS hardware in WSN application is still unattainable. The target tracking systems which are already in use estimating the position of moving target based on measurements on Received Signal Strength (RSS), Time of Arrival (TOA), Angle of Arrival (AOA) and Time Difference of Arrival (TDOA). These measurements are less prudent for the application, which requires highly accurate target tracking. This paper proposes a Target Movement Prediction Algorithm (TMPA) based on topological coordinates. TMPA uses Topological Preserving Maps (TPM) to track and navigate the location of the target and Adaptive Weighted Target Tracking (AWTT) methodology condenses fault and improves the accurateness in the prediction. Our simulation results show that the time taken to identify the target movements is considerably low and improvement in prediction ratio.