Optimization and localization based framework for priority-aware node ranking and routing in IoT-driven acoustic systems

Abstract

Beacon node ranking is crucial for minimizing energy consumption and optimizing data routing in IoT-driven Underwater Acoustic Sensor Networks (UASN), where accurate node positioning is essential for applications such as underwater robotics, unmanned autonomous vehicles, and location-based services. This research presents an efficient beacon node ranking framework by integrating a modified PageRank mechanism with bio-inspired metaheuristic approaches, including Ant Colony Optimization (ACO) for optimal path selection, Artificial Bee Colony (ABC) for identifying high-fitness nodes, and Fish School Search (FSS) for optimal node selection. To enhance localization accuracy, the PageRank parameters are optimized through metaheuristic hybridization using an adaptive mathematical approach, while key performance indicators such as energy reduction, localization error, and route optimization are evaluated over multiple iterations. The proposed Priority Ranking Algorithm for Localization (PRAL) is designed for energy-efficient localization in both obstacle-free and obstacle-rich environments, assessing localization error, success ratio, ineffective position rate, and average localization time per node. Network Simulator-v3.35 and the AquaSim framework, PRAL demonstrates significant improvements over baseline methods (MBIL, LoMoB, LSMB, etc.), achieving a 6 % higher localization success rate (98 %), a 3.7 % reduction in localization error, an 18 % decrease in energy consumption, a 20 % increase in network lifetime, and a 15 % improvement in obstacle-handling efficiency. This framework enhances data transfer reliability, positioning accuracy, and overall network performance in deep-sea environments by effectively mitigating localization and energy consumption challenges.