An improved target tracking scheme based on MC-MPMC method for mobile wireless sensor networks

Target tracking is crucial to many applications in wireless sensor networks (WSNs). Existing tracking schemes used in WSNs can basically be classified two categories, clustering and predicting. Considering network clustering consumes much energy for limited-energy WSNs, a predicting target tracking scheme is proposed called MC-MPMC (measurement compensation-based mixture population Monte Carlo) which tracks the target based on predicted locations in this work. Adaptive mixture PMC model for generating proposals varying from each iteration is proposed to guarantee sampling diversity. And also, extra measurements or observations generating method is introduced to compensate missed prediction locations or false estimations, avoiding tracking behavior degradation. Firstly, samples drawn from the proposals of next iteration can be generated by a mixture method to avoid sample degeneracy. Secondly, sample weights are jointly computed based on adaptive fusion of compensation measurement and true measurements. Thirdly, HTC method is combined to MC-MPMC scheme to decrease energy consumption in WSNs. Then, the proposed method is verified through comprehensive experiments about tracking error, delay and consumption predictions. Moreover, performance comparisons of MC-MPMC with other tracking schemes are also proposed.

Automated summary

In this research, a target tracking scheme based on predicted locations is proposed. An adaptive mixture PMC model is used to generate diverse proposals, and an additional measurements method is introduced to compensate for false estimations. The HTC method is also combined to reduce energy consumption, and the performance of the proposed method is verified through experiments.