Hybrid Optimization Algorithm for Density Based Cluster Head Selection in Wireless Sensor Networks

Abstract

Wireless sensor networks (WSN) are a strategy that is emerging, presenting persistent computation for specific applications. The most significant possible concern with wireless sensor networks (WSNs) is energy efficiency because they use a small number of non- rechargeable, non-replaceable batteries. Clustering is a common method for constraining energy in a wireless sensor network. Furthermore, choosing an efficient in respect of energy Cluster Head (CH) selection is essential for effective clustering. The methodology for optimizing the hybrid Firefly and Glowworm Swarm method is used as the foundation for implementing the cluster head selection technique provided in this paper. During the clustering process, cluster heads are selected based on their primary selection features. Furthermore, demonstrate Cluster Formation takes place when sensor nodes without cluster heads join respective CHs depending on the resulting mass functions that can implement the basic characteristics of Density-Based Spatial Clustering of Applications with Noise (DBSCAN), one of the Density-based Clustering algorithm.