Merging and incentive-based techniques in hybrid clustering for multi-target tracking in Wireless Sensor Networks

In this paper, we extend our previous work in which we proposed an efficient merging method to integrate dynamic clusters in a hybrid multi-target tracking clustering (HCMTT) algorithm. In this method, the tracking task is switched between static clusters as the backbone of the network and dynamic clusters, which form in boundary regions. Our proposed merging mechanism reduces energy dissipation where intersecting dynamic clusters appear. In this paper, we propose an incentive-based mechanism for dynamic cluster dismissal which gently applies to zigzag movement models of targets by considering the ingress and egress traffic of targets. Applying the new incentive-based method, we observed a 22% reduction of power consumption in HCMTT. Satisfying results are also obtained for tracking quality of our method compared to a prediction-based method, DPT.