Redundant ClusterHead Selection and Quality of Data Reconstruction Through Clustering for Large-Scale WSNs

Abstract

WSN is a large network that consists of a group of spatially distributed sensors nodes. Sensor nodes are partial in power, computational capacities and memory. Sensor nodes compactly installed to monitor physical or environmental conditions, such as temperature, pressure, pollutants. This study examines a Cluster Head Selection (CHs) and takes the CH with the maximum outstanding energy node and less broadcast distance between the CH and BS. It discovered ideal stability between data quality, energy expenditure, and community management ease. The key decision is that the proposal of WSN algorithms must be processing-oriented. i.e., the process of energy on both the Clustering and in-network processing, which ensures both energy efficiency and data quality. Hence, it is more operational to achieve the wireless sensor networks' major loads, which persist the network lifetime.