Energy aware clustering and routing for wireless sensor networks using metaheuristics optimization algorithms

Abstract

Wireless sensor networks (WSNs) and their applications reduce energy consumption to enhance the lifetime of the network. WSN applications require energy-efficient communication, but the network includes resource constraint tiny sensors across monitoring area. Data processing, aggregation, and clustering are energy-intensive, and deployment and discovery of nodes are complex. In order to overcome certain issues in WSN application, optimal selection of fine cluster heads (FCHs) and cluster heads (CHs) and optimal routes to the base station (BS) are proposed, still lot of optimizations are essential to enhance lifetime of the network. This research paper employs bacterial foraging and ant colony optimization (BFACO) to identify optimal gateways (FCHs), CHs, and energy-efficient paths from sensor modes to BS while optimizing node energy consumption. The algorithm dynamically creates routing paths with lower network overhead, enabling long-term network utilization and enhanced lifetime. Performance is evaluated against parallel research algorithms using metrics such as energy-usage, network lifespan, and packet delivery ratio (PDR). An energy-usage (52.8% of energy reduction over best case), packet delivery ratio (8.7% of enhancement over best case), and network lifetime (4.3% of rounds over best case) are WSN performance outcomes for 300 sensors. The BFACO using a clustering and routing algorithm outperforms over parallel research.