A Novel Gaussian Mixture Model Clustering with Hierarchical Routing (GMMCHR) for Wireless Sensor Network

Among the technologies developing most rapidly are wireless sensor net-works (WSNs). This is due to WSNs possessing numerous applications and a remarkably robust set of capabilities. This study addresses the critical issue of energy conservation in Wireless Sensor Networks (WSNs), where limited energy capacity of Sensor Nodes (SNs) significantly impacts network lifespan. To enhance energy efficiency, a novel hierarchical routing algo-rithm named GMMCHR (Gaussian Mixture Model Clustering with Hierar-chical Routing) is proposed. The method utilizes the GMM algorithm for clustering and introduces a hierarchical packet routing mechanism based on Central Cluster Heads (CCH) and Direct Cluster Heads (DCH), selected us-ing various Fitness Functions (FFs). Simulations were conducted in two sce-narios—100 nodes in a 100×100 m² area and 200 nodes in a 200×200 m² ar-ea—using MATLAB. Results demonstrate that GMMCHR significantly re-duces energy consumption, improves network coverage, and extends the overall network lifetime, validating its effectiveness in energy-efficient rout-ing for WSNs.