WSN16-5: Distributed Formation of Overlapping Multi-hop Clusters in Wireless Sensor Networks

Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Most of the published clustering algorithms strive to generate the minimum number of disjoint clusters. However, we argue that guaranteeing some degree of overlap among clusters can facilitate many applications, like inter-cluster routing, topology discovery and node localization, recovery from cluster head failure, etc. We formulate the overlapping multi-hop clustering problem as an extension to the k-dominating set problem. Then we propose MOCA; a randomized distributed multi-hop clustering algorithm for organizing the sensors into overlapping clusters. We validate MOCA in a simulated environment and analyze the effect of different parameters, e.g. node density and network connectivity, on its performance. The simulation results demonstrate that MOCA is scalable, introduces low overhead and produces approximately equal-sized clusters.