Design of a Hybrid Bio-Inspired Model for Energy Harvesting Networks

Energy harvesting for maximizing battery efficiency is a pivotal task in wireless sensor networks. Sustainable sources of energy are converted into electrical energy with an energy harvesting circuit. There is a need for fundamentally efficient models that allow for low-energy operations. Energy distribution modeling is required while designing energy-collecting networks, but they are susceptible to internal and external defects, which increase energy consumption. The proposed model uses a Genetic Algorithm (GA) based on their residual energy and distance metrics. Particle Swarm Optimization (PSO) is a technique for processing clustered nodes that assists in the deployment of a fault-tolerant and energy-harvesting process. The PSO method is further enhanced by combining an Ant Colony Optimization (ACO) model and Teacher Learner Based Optimization (TLBO), which aids in value-based defect identification and mitigation activities.