A Novel Intelligent Tracking Framework for Wireless Sensor Networks based on Sammon Regularization and Circumference-Reinforced Projection Pursuit

Abstract

In recent years, Wireless Sensor Networks (WSNs) used in extensive application ranging between military, surveillance, smart cities and so on. Target object tracking is one of the most fascinating applications in this domain of interest that specifically comprises of detecting the target object and keeps tracking of their movements. Several methods have been developed for target object tracking in WSN with minimal energy consumption. However, the accuracy level was not increased by existing tracking techniques. In order to address these problems, a novel targets object tracking method called Gaussian Distributive Sammon Regularization Policy (GG-DSRP) for efficient target object tracking in WSN is proposed. To make tracking more accurate another novel is also proposed called Circumference Reinforced Projection Pursuit and Adaptive Boosting (CRPP-AB) for tracking target object in WSN. The GG-DSRP method consists of three major processes namely reference node selection, target detection, and trajectory prediction. For this process Wilcoxon rank-sum test for identifying the reference node based on higher residual energy is applied in the first stage and in final stage, the target object trajectories are identified using Sammon projective on-policy learning algorithm to predict the target trajectories based on the state transition property. In CRPP-AB, node selection is done with Circumference Reinforced Acceleration and Adaptive Boost Target Object Classification is carried out to identify the target trajectory in WSN with Soft-Margin Support Vector Machine as the weak learners. Experimental evaluation is carried out on factors such as energy consumption and target object tracking accuracy with respect to different number of sensor nodes and data packets.