Mean Shift Based Collaborative Localization with Dynamically Clustering for Wireless Sensor Networks

Mean shift is a nonparametric method to estimate the density gradient, which iteratively computes the mean of observations and shifts to the centroid until reach a predefined end condition. This paper proposes a mean shift based scheme for localization and tracking in wireless sensor networks.Mean shift is a generalized framework, and range-free centroid and range-based W-Centrod are both the special cases of this generalized framework. A dynamically clustering protocol is proposed to support the generalized framework. Analysis and simulation results show that the communication overhead and jitter are reduced to 1/3 of traditional algorithms. The positioning accuracy of proposed algorithm with optimal weight increases 28% and 11% in comparison to Centroid and W-Centroid.