ReSCUE: Relatively Stable Clustering for Unbiased Environments in VANETs

Clustering algorithms improve network manageability through several topology partitioning techniques. In some particular cases, such as vehicular ad hoc network (VANETs) communications, significant performance improvements can be introduced via clustered networking solutions whereas merging clusters for the sake of scalability may lead to degraded network stability. In this paper, we explore the impact of merging clusters, and furthermore based on these results, we propose a new clustering technique, namely Relatively Stable Clustering for Unbiased Environments (ReSCUE). The objective of ReSCUE is primarily guaranteeing cluster stability in an unbiased manner. ReSCUE keeps track of the spatio-temporal changes in VANET node characteristics, and uses these characteristics along with local information to prevent biased clustering which is based on common and general node characteristics. We evaluate the performance of ReSCUE through simulations and show that ReSCUE can form relatively more stable clusters while reducing the frequency of cluster merges, as well as that of the node status changes.