BICOA: A Bio-Inspired Cooperative Optimization Algorithm for Data Aggregation in Heterogeneous Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are at the core of contemporary IoT ecosystems, supporting timely data retrieval in various applications like aerospace, factory automation, and ecological surveillance.Optimizing WSNs, especially in heterogeneous environments with diverse node abilities, is still a pressing problem.Effective energy consumption regulation and careful selection of Cluster Head (CH) are important to maximize network lifetime.This paper presents BICOA, an innovative Bio-Inspired Cooperative Optimization Algorithm that synergistically integrates Bees Colony Optimizer (BCO) with Grey Wolf Optimizer (GWO) and is further supplemented with adaptive machine learning techniques.Rigorous simulation results prove that BICOA is more efficient than other existing techniques like SEP, DEEC, Z-SEP, and PSO-ECSM, with remarkable gains in various performance factors.For example, with parameter combinations m=0.2 and a=3.0,BICOA realized an average network lifetime of 185 rounds, which is 37% more with SEP (135 rounds), DEEC (142 rounds), Z-SEP (148 rounds), and PSO-ECSM (150 rounds).BICOA used an average energy of 12.5 Joules of energy by each node throughout the simulation time, as opposed to SEP (15.3),DEEC (14.7),Z-SEP (14.9), and PSO-ECSM (15.1).BICOA also registered a throughput of 7800 bits/s, lowered latency to 0.12 seconds, and reduced dead nodes to 12 by round, far outperforming similar schemes.The outcomes demonstrate BICOA's scalability, versatility, and impressive performance in heterogeneous WSNs, paving way for even more powerful, energy-conserving, and adaptive networks in complicated and resource-sparse IoT applications.