Cluster density of dependent thinning distributed clustering class of algorithms in ad hoc deployed wireless networks

Abstract

Distributed clustering is widely used in ad hoc deployed wireless networks. Distributed clustering algorithms like DMAC, HEED, MEDIC, ANTCLUST-based, and EDCR produce well-distributed Cluster Heads (CHs) using dependent thinning techniques where a node’s decision to be a CH depends on the decision of its neighbors. An analytical technique to determine the cluster density of this class of algorithms is proposed. This information is required to set the algorithm parameters before a wireless network is deployed. Simulation results are presented in order to verify the analytical findings.