BCEWN: Design of a Hybrid Bioinspired Clustering Model for Deployment of Energy-Aware Wireless Networks

A low-power wireless network design is an iterative process that integrates multiple optimization techniques to identify and mitigate redundant network operations. Existing power-aware models either showcase higher complexity or require larger information sets, which limits their scalability for real-time networks. Moreover, these models work well under an elaborative set network configuration but cannot be used for general-purpose networks. The study suggests creating a novel hybrid bioinspired clustering model for energy-aware wireless network deployment to address these problems. The proposed model initially collects limited network information sets, including approximate node locations, residual energy levels, temporal throughput, and packet delivery levels. These sets are processed via a Grey Wolf Optimizer (GWO), which performs initial binary-clustering operations. These binary clusters are generated by iterative identification of high-energy nodes between a given set of source & destination pairs. Results of the clustering process are used to train a Particle Swarm Optimizer (PSO) that uses the temporal information sets to identify energy-aware routing paths. The PSO models a temporal fitness function capable of reducing redundant node selections, thereby improving network lifetime even under many communication requests. Performance of the GWO-clustering & PSO-routing model was validated under large-scale scenarios, and it was observed that the proposed model reduced energy consumption by 8.3% while improving communication speed by 3.2% with a 4.5% higher data rate and 2.9% higher packet delivery performance under real-time heterogeneous network scenarios.