Improve WSN Lifetime Based on K-Means, Genetic Clusters, and Data Compression

Abstract

Environmental monitoring and industrial process automation are dependent on wireless sensor networks (WSNs). The limited power supply of WSNs' sensor nodes makes energy efficiency difficult.   The key goals are selecting cluster heads (CH), distributing nodes, transmitting data, and compressing data. Genetic algorithms improve CH selection. This method incorporates residual energy, base station (BS) distance, and communication overhead. Network lifetime and energy efficiency are maximised by the selection of genetic algorithms. The sensor nodes are distributed using K-means clustering to share load and energy consumption among clusters in a balanced way. Our study's multi-hop data transmission mechanism sends compressed data packets to the base station.  Multi-hop communication reduces sensor device energy use.  Intermediary nodes for data forwarding significantly reduce network energy usage. To save energy, we suggest implementing a compressed data packet transmission technique. Compression methods minimize data packet size while keeping data precision, improving sensor network energy efficiency. This sustains the network's longevity.  Our proposed method has been extensively simulated for energy usage, network longevity, and data delivery ratio.  The results show 100% optimization over LEACH, LEACH-C, FIGWO, PSO, ABC-SD, CGTABC2 & ACO, ED-LEACH, I-LEACH, CBDAS, GHND, R-LEACH, MH-LEACH, D-LEACH, 98% for ADMH-LEACH. This study optimizes sensor efficiency in wireless networks by conserving energy in the LEACH protocol. It helps design resilient and efficient WSNs, enabling sensor-driven applications in energy-constrained environments.