Group Mobility Based Clustering Algorithm for Mobile Ad Hoc Networks

Recent research activities have recognized the essentiality of node mobility for the creation of stable, scalable and adaptive clusters with good performance in mobile ad hoc networks (MANETs). In this paper, we propose a distributed clustering algorithm based on the group mobility and a revised group mobility metric which is derived from the instantaneous speed and direction of nodes. Our dynamic, distributed clustering approach use Gauss Markov group mobility model for mobility prediction that enables each node to anticipate its mobility relative to its neighbors. In particular, it is suitable for reflecting group mobility pattern where group partitions and mergence are prevalent behaviors of mobile groups. We also take the residual energy of nodes and the number of neighbor nodes into consideration. The proposed clustering scheme aims to form stable clusters by reducing the clustering iterations even in a highly dynamic environment. Simulation results show that the performance of the proposed framework is superior to two well-known clustering approaches, the MOBIC and DGMA, in terms of average number of clusterhead changes.