As node energy of wireless sensor networks (WSN) is limited and cannot be supplemented after exhaustion, clustering algorithm\nis frequently taken as an effective method to prolong the lifetime of WSN. However, the existing clustering algorithms have some\ndrawbacks, either consuming excessive energy as a result of exchanging too much controlling information between nodes, or\nlacking a comprehensive perspective in terms of the balance among several conflicting objectives. In order to overcome these\nshortcomings, a novel combinatorial optimization-based clustering algorithm (COCA) for WSN is proposed in this paper.\nDifferent from the above mentioned algorithms which take clustering as a continuous optimization problem, COCA solves the\nclustering problem from the perspective of combinatorial optimization. Firstly, the clustering of WSN is abstracted into a\ncombinatorial optimization problem. Then, the binary particle coding scheme of cluster head is proposed, which is based on the\ncorresponding relationship between nodes and particle position vectors, and the fitness function is designed according to the\nparameters used in the process of cluster formation. Finally, the binary particle swarm optimization algorithm is applied to\nimplement the clustering. COCA is validated under different scenarios compared with three other clustering algorithms. The\nsimulation results show that COCA has better performance than its comparable algorithms.
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