Coverage hole detection using social spider optimized Gaussian Mixture Model

Wireless sensors are backed by their battery life. The battery depletion creates a communication hole in the network. The replacement of certain energy-depleted sensor nodes necessitates their detection. The work in this article presents a novel solution for the detection of coverage hole in the WSN. An undirected graph was employed in this study to divide the WSN region into two parts: the outer and inner nodes. The Gaussian Mixture Model (GMM) clustering detects the outer/edge nodes at the coverage hole area. Node degree, closeness, betweenness, and page rank are the four graph metrics that GMM uses for clustering in this work. GMM’s clustering performance is governed by the static mean and covariance. A novel combination of social spider optimization and GMM is then leveraged to fine-tune the cluster to decrease false detection rates of holes. Distinct holes, such as round, E-shape, and rectangular, are used to conduct the analysis. Using the SSO-EM GMM approach, the comparison shows that it outperforms the current methods of coverage hole identification.