Adaptive Fuzzy Energy-Efficient Clustering and Energy Optimization Protocol for Underwater Wireless Sensors Networks

Abstract

Research on underwater wireless sensor networks (UWSNs) has been significant for applications such as forecasting adversity and disaster, hydrological and military surveillance, seepage monitoring, and underwater triangulation. These networks however, face challenges like significant delay spread, soaring interference, noise, jarring environments, poor connectivity, and restricted battery life. They also cause significant problems in terms of energy efficiency and network longevity. Nodes in UWSNs are subject to additional limitations, including fluctuating ambient conditions, large propagation delays, and limited energy supplies. Designing routing protocols for UWSNs is a promising solution to overcome these issues. The Adaptive Fuzzy Energyefficient Clustering and Energy Optimization (AFECEO) protocol, proposed especially for UWSNs, is thoroughly evaluated in this study in comparison to six popular clustering protocols: GEC, LEACH, PEGASIS, DCHS, DEEC, and LGCA. The fuzzy logic-based adaptive clustering process used by AFECEO dynamically chooses cluster heads by taking into account variables including distance to the sink, node residual energy, and underwater communication difficulties such as acoustic signal attenuation. According to simulation data, under various underwater settings, AFECEO performs better than its competitors in a number of critical performance parameters, such as average remaining energy, dead node count, and network longevity. Notably, AFECEO outperforms conventional protocols in terms of residual energy by up to 45% and dead node reduction by 60 %, guaranteeing improved energy optimization and dependable data transfer in UWSNs. This study demonstrates how well AFECEO works as a reliable option for energy-efficient communication in submerged settings, opening the door for more advanced monitoring and exploration uses.