An Energy-Efficient Data Aggregation Clustering Algorithm for Wireless Sensor Networks Using Hybrid PSO

Extending the lifetime of wireless sensor networks (WSNs) and minimizing energy costs are two most significant concerns for data transmission. Sensor nodes powered by their own battery capacity, allowing them to perform critical tasks interact with other nodes. The quantity electricity saved from each together in a WSN has been strongly linked network’s longevity. Clustering conserves power transmission, but absence mechanism selecting suitable cluster head (CH) node increases complexity collection usage Additionally, disparity consumption can lead premature demise nodes, reducing lifetime. Metaheuristics used solve non-deterministic polynomial (NP) lossy clustering problems. primary purpose this research is enhance efficiency network endurance WSNs. To address issue, work proposes solution where hybrid particle swarm optimization (HPSO) paired improved low-energy adaptive hierarchy (HPSO-ILEACH) CH selection cases aggregation order increase maximize stability WSN. In approach, HPSO determines CH, distance between cluster’s member residual Then, ILEACH minimize expenditure during process adjusting CH. Finally, HPSO-ILEACH algorithm was successfully implemented aggregating saving energy, its performance compared three algorithms: low energy-adaptive (LEACH), (ILEACH), enhanced PSO-LEACH (ESO-LEACH). results simulation studies show that increased lifetime, an average 55% staying alive, while 28% mentioned techniques.