In wireless sensor networks, sensor nodes are usually powered by battery and thus have very limited energy. Saving\nenergy is an important goal in designing a WSN. It is known that clustering is an effective method to prolong network\nlifetime. Due to the development of big data, there are more sensor nodes and data needed to process. So how to\ncluster sensor nodes cooperatively and achieve an optimal number of clusters in a big data WSN is an open issue. In\nthis paper, we first propose an analytical model to give the optimal number of clusters in a wireless sensor network.\nWe then propose a centralized cluster algorithm based on spectral partitioning method. After that, we present a\ndistributed implementation of the clustering algorithm based on fuzzy C-means method. Finally, we conduct\nextensive simulations, and the results show that the proposed algorithms outperform the hybrid energy-efficient\ndistributed (HEED) clustering algorithm in terms of energy cost and network lifetime.
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