The Overlapped K-hop (OK) Clustering Algorithm

Abstract

Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Clustering algorithms are mostly heuristic in nature and aim at generating the minimum number of disjoint clusters. In this report, we formulate the overlapping multi-hop clustering problem as an extension to the k-dominating set problem. Then we propose a fast, randomized, distributed multi-hop clustering algorithm (OK) for organizing the sensors in a wireless sensor network into overlapping clusters with the goal of minimizing the overall communication overhead, and processing complexity. OK assumes a quasi-stationary network where nodes are location-unaware and have equal significance. No synchronization is needed between nodes. OK is scalable; the clustering formation terminates in a constant time regardless of the network topology or size. The protocol incurs low overhead in terms of processing cycles and messages exchanged. We analyze the effect of different parameters (e.g. node density, network connectivity) on the performance of the clustering algorithm in terms of communication overhead, node coverage, and average cluster size. The results show that although we have overlapped clusters, the OK clustering algorithm still produces approximately equal-sized clusters.