CLIQUE: Role-Free Clustering with Q-Learning for Wireless Sensor Networks

Abstract

Clustering and aggregation inherently increase wireless sensor network (WSN) lifetime by collecting information within a cluster at a cluster head, reducing the amount of data through computation, then forwarding it. Traditional approaches, however, both spend extensive communication energy to identify the cluster heads and are inflexible to network dynamics such as those arising from sink mobility, node failure, or dwindling battery reserves. This paper presents Clique, an approach for data clustering that saves cluster head selection energy by using machine learning to enable nodes to independently decide whether or not to act as a cluster head on a per-packet basis. We refer to this lack of actual cluster head assignment as being role-free, and demonstrate through simulations that, when combined with learning dynamic network properties such as battery reserves, up to 25% less energy is consumed in comparison to a traditional, random cluster head selection approach.