Distributed robust data clustering in wireless sensor networks using diffusion moth flame optimization

Abstract Traditional K-Means based distributed clustering used in wireless sensor networks has limitation of getting stuck into local minima, thus many times results in giving inaccurate cluster partitions. To alleviate this drawback, evolutionary based robust distributed clustering techniques are proposed in this paper. These techniques have the capability to determine the global optima thus results in effective cluster partitioning. A moth flame optimization based method is proposed here which minimizes the intra-cluster distance to determine the optimal partition at every sensor node. A diffusion method of cooperation is employed by sharing the best moth position and corresponding fitness value (intra cluster distance) to the neighboring nodes. To introduce robustness, a weight based method for detection and removal of outliers is employed. In this method a weight based on volume and density is given to each data point for outlier detection, a larger weight is considered as an outlier. The simulation study is carried out on one synthetic and two real datasets. The performance of proposed approach is compared with diffusion particle swarm optimization (DPSO), diffusion whale optimization algorithm (DWOA), diffusion elephant herding optimization (DEHO) and distributed K-Means (DK-Means) in terms of Dunn’s index, Silhouette index and time complexity. The minimum average Euclidean deviation of proposed diffusion moth flame optimization is 7.16%, 3.25%, 5.24% and 21.70% lower compared to DPSO, DWOA, DEHO and DK-Means respectively for cook agronomy farm dataset.

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