An AUV Path Planning and Data Collection Optimization Algorithm Based on Tetrahedral Decomposition and Fermat Point

Autonomous Underwater Vehicles (AUVs) play a critical role in data collection for Underwater Acoustic Sensor Networks (UASNs) within the maritime Internet of Things (IoT). However, challenges such as limited energy resources, high latency, and the unique characteristics of underwater communication hinder efficient data collection. This paper introduces a novel and geometrically optimized AUV path planning and data collection optimization algorithm (TDFPQ), which is the first to integrate tetrahedral decomposition and Fermat point theory into AUV path planning. The proposed method first partitions the monitoring area and employs tetrahedral decomposition to determine optimal hovering points for AUVs, minimizing path loss and improving communication reliability. Subsequently, a genetic algorithm is applied to optimize the AUV’s visiting sequence, modeled as a Vehicle Routing Problem (VRP). To further enhance data collection efficiency, a Q-learning-based strategy dynamically adjusts the data transmission process by balancing data volume, energy consumption, and channel conditions. Simulation results demonstrate that the proposed algorithm significantly improves data collection efficiency while reducing energy consumption compared to state-of-the-art method, providing a solution for AUV-assisted data collection in UASNs.