Adaptive Fuzzy Game-Based Energy-Efficient Localization in 3D Underwater Sensor Networks

Numerous applications in 3D underwater sensor networks (UWSNs), such as pollution detection, disaster prevention, animal monitoring, navigation assistance, and submarines tracking, heavily rely on accurate localization techniques. However, due to the limited batteries of nodes difficulty for energy harvesting UWSNs, it is challenging localize successfully within a short node lifetime an unspecified environment. Therefore, we propose Adaptive Energy-Efficient Localization Algorithm (Adaptive EELA) enable energy-efficient while adapting dynamic environment changes. EELA takes fuzzy game-theoretic approach, whereby Stackelberg game used model interactions among anchor UWSNs employs adaptive neuro-fuzzy method set appropriate utility functions. We prove that socially optimal Stackelberg–Nash equilibrium achieved EELA. Through extensive numerical simulations under various environmental scenarios, evaluation results show our proposed algorithm accomplishes significant reduction, e.g., 66% lower compared baselines, achieving desired performance level terms coverage, error, delay.