Energy efficient data fusion approach using squirrel search optimization and recurrent neural network

Abstract

Sensor networks have helped wireless communication systems. Over the last decade, researchers have focused on energy efficiency in wireless sensor networks. Energy-efficient routing remains unsolved. Because energyconstrained sensors have limited computing capabilities, extending their lifespan is difficult. This work offers a simple, energy-efficient data fusion technique employing zonal node information. Using the witness-based data fusion technique, the evaluated network lifetime, energy consumption, communication overhead, end-to-end delay, and data delivery ratio. Energyefficient data fusion optimizes energy utilization using squirrel search optimization and a recurrent neural network. The method allows the system to recognize a sensor with excessive energy dissipation and relocate data fusion to a more energy-efficient node. The proposed model was compared against artificial neural network-particle swarm optimization (ANN-PSO), cuckoo optimization algorithm-back propagation neural network (COABPNN), Elman neural network-whale optimization algorithm (ENN-WOA), and extreme learning machine-particle swarm optimization (ELM-PSO). The model achieved 94.50% network lifetime, 26.63% communication overhead, 93.85% data delivery ratio, 10.50 ms end-to-end delay, and 282 J energy usage.