Wireless Sensor Network Coverage Hole Localization by Ant Colony Optimized Gaussian Mixture Model Clustering

摘要

Nodes in the wireless sensor network have a minimal power source and they exhaust very quickly in communicating with each other. If any of the nodes die, a coverage hole creates in that region. This coverage hole leads to fast energy depletion of other nodes along with the security issues due to intruder node's placement at that location. The solution to detection of coverage hole is discussed in our paper and it is experimentally validated. We propose an unsupervised machine learning clustering algorithm to cluster the network graph metrics. An undirected network graph of nodes is created and five graph metrics are extracted. The vector of features is clustered by Ant colony optimized expectation-maximization Gaussian mixture model (ACO-EM GMM) clustering algorithm. Our algorithm is compared with the state of art works based on false detection parameter.