Incremental clustering-based object tracking in wireless sensor networks

Abstract

Emerging significance of moving object tracking has been actively pursued in the Wireless Sensor Network (WSN) community for the past decade. As a consequence, a number of methods from different angle of assessment have been developed while relatively satisfying performance. Amongst those, clustering based object tracking has shown significant results, which in term provides the network to be scalable and energy efficient for large-scale WSNs. As of now, static cluster based object tracking is the most common approach for large-scale WSN. However, as static clusters are restricted to share information globally, tracking can be lost at the boundary region of static clusters. In this paper, an Incremental Clustering Algorithm is proposed in conjunction with Static Clustering Technique to track an object consistently throughout the network solving boundary problem. The proposed research follows a Gaussian Adaptive Resonance Theory (GART) based Incremental Clustering that creates and updates clusters incrementally to incorporate incessant motion pattern without defiling the previously learned clusters. The objective of this research is to continue tracking at the boundary region in an energy-efficient way as well as to ensure robust and consistent object tracking throughout the network. The network lifetime performance metric has shown significant improvements for Incremental Static Clustering at the boundary regions than that of existing clustering techniques.