Evaluating Energy-Efficient AUV Path Planning and Data Collection: Insights from a Novel Benchmark Study in UWSNs

Underwater Wireless Sensor Networks (UWSNs) play a significant role in marine applications, including environmental monitoring, ocean floor mapping, and disaster response. However, the energy constraints of sensor nodes and Autonomous Underwater Vehicles (AUVs), as well as the high cost of underwater acoustic communication, present major design challenges. This paper introduces Energy-Conscious Optimization of AUVs (ECO-AUV), a new framework that uses energy-aware K-Means clustering and the A* heuristic search algorithm to improve underwater data collection. ECO-AUV is specially designed to minimize total energy expenditure by improving intra-cluster communication procedures and AUV navigation paths. The framework provides reliable data collection and transfer through dynamic route planning that accounts for environmental conditions such as ocean currents and seafloor anomalies. Extensive simulations were carried out to compare ECO-AUV with two recent hybrid methods: Particle Swarm Optimization (PSO) with Genetic Algorithms (GAs), and Artificial Bee Colony (ABC) with Ant Colony Optimization (ACO). Results demonstrate that ECO-AUV is significantly more energy-efficient, creates more optimized traversal patterns, offering a high Packet Delivery Ratio (PDR) of 98.5%. Additionally, the framework exhibits low computational complexity, enabling its application in real-time applications. These results establish ECO-AUV as a scalable, energy-efficient solution for strong underwater sensing and communication.