Developing a resilient localisation system for wireless sensor networks in underwater environments

Localization is a critical challenge in underwater wireless sensor networks (UWSNs) due to the unique and harsh aquatic environment, characterized by high signal attenuation, multipath interference, and dynamic node mobility. Traditional localization techniques relying solely on acoustic signals face limitations in accuracy, energy consumption, and real-time adaptability. This research presents a novel resilient localization system that integrates hybridized communication methods acoustic, radio frequency (RF), and optical signaling to enhance the efficiency and reliability of underwater node positioning. The developed system leverages the complementary strengths of each communication modality, dynamically selecting the most optimal method based on environmental conditions and network constraints. Acoustic signals provide long-range but low-data-rate localization, RF signals facilitate medium-range data transmission in specific underwater conditions, and optical communication ensures high-speed, short-range localization with minimal latency. A robust fusion algorithm, incorporating machine learning-based predictive modelling and error-correction techniques, is developed to enhance localization precision while mitigating environmental distortions. Extensive simulations and real-world experimental deployments validate the system\'s effectiveness. Performance metrics, including localization accuracy, energy efficiency, and communication latency, are analyzed under varying water conditions, demonstrating significant improvements over conventional single-modality localization approaches. The findings indicate that the hybridized system enhances positioning accuracy by up to 35%, decreases energy consumption by 27%, and reduces communication latency by 20%, contributing to developing sustainable and resilient UWSNS. The developed model lays the foundation for future advancements in autonomous underwater vehicle (AUV) navigation, deep-sea sensing applications, and next-generation UWSN deployments.