Using Hybrid Genetic Algorithm for Data Aggregation in Wireless Sensor Networks

• Published in: Proceedings of the 2024 18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024
• Main Author: Sharmin S.; Ahmedy I.; Noor R.M.; Ismail H.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186145788&doi=10.1109%2fIMCOM60618.2024.10418358&partnerID=40&md5=9932f4fd34372481da6a9a33e86324e0
• DOI: 10.1109/IMCOM60618.2024.10418358

Efficient energy usage is vital for extending Wireless Sensor Networks (WSNs) lifespan. While Improved LowEnergy Adaptive Clustering Hierarchy (ILEACH) excels in energy-efficient data aggregation, challenges like premature cluster head (CH) failure remain. Genetic Algorithm (GA) optimizes parameters, including energy, in WSNs. We propose a novel hybrid ILEACH-GA algorithm for data aggregation. ILEACH forms clusters, GA evaluates fitness, selecting optimal clusters for aggregation. GA mitigates ILEACH's premature CH failure. ILEACH-GA surpasses LEACH, ILEACH, and GA-LEACH, with significantly higher throughput (10.0%, 47.4%, 21.9 respectively), retaining higher residual energy (0.0805) and alive nodes (25.5%). This innovation boosts sustainable WSN data aggregation, overcoming limitations, and enhancing performance. This innovation elevates sustainable WSN data aggregation, surmounting limitations, and augmenting performance, applicable in waste and crop management systems. © 2024 IEEE.