An Underwater Localization Algorithm Based on the Internet of Vessels

Localization is vital and fundamental for underwater sensor networks. However, the field still faces several challenges, such as the difficulty of accurately deploying beacon nodes, high deployment costs, imprecise underwater ranging, and limited node energy. To overcome these challenges, we propose a crowdsensing-based underwater localization algorithm (CSUL) by leveraging the computational and localization resources of vessels. The algorithm is composed of three stages: crowdsensing, denoising, and aggregation-based optimization. In the crowdsensing stage, nodes transmit localization requests, which are received by vessels and broadcasted to nearby vessels. Using concentric circle calculations, the localization problem is transformed from a three-dimensional space to a two-dimensional plane. An initial set of potential node locations, termed the concentric circle center set, is derived based on a time threshold. The denoising stage employs a Density-Based Noise Removal (DBNR) algorithm to eliminate noise caused by vessel mobility, environmental complexity, and the time threshold, thereby improving localization accuracy. Finally, in the aggregation-based optimization stage, the denoised node location set is refined using a centroid-based approximate triangulation (CBAT) algorithm to determine the final node location. Simulation results indicate that the proposed method achieves high localization coverage without requiring anchor nodes and significantly improves localization accuracy. Additionally, since all localization computations are carried out by vessels, node energy consumption is greatly reduced, effectively extending the network’s lifetime.