03025nas a2200289   4500000000100000000000100001008004100002260001200043653002800055653003400083653003100117653002200148653002200170653002400192653002700216653002800243653002600271653004000297653002100337100001800358245013700376856005800513300001000571490000600581520213400587022001402721       2024                            d  c09/202410aArtificial Intelligence10aAutonomous Underwater Vehicle10aEnergy-aware Path Planning10aMaritime Commerce10aMaritime Industry10aMaritime Operations10aOptimization Algorithm10aShip Management Systems10aSafe Sailing Planning10aUnderwater Wireless Sensor Networks10aWater Monitoring1 aTayfun Acarer00aEnergy-Aware Path Planning by Autonomous Underwater Vehicle in Underwater Wireless Sensor Networks for Safer Maritime Transportation  uhttps://www.ijimai.org/journal/bibcite/reference/3476  a15-270 v83 aThroughout history, maritime transportation has been preferred for international and intercontinental trade thanks to its lower cost than other transportation ways, which have a risk of ship accidents. To avoid these risks, underwater wireless sensor networks can be used as a robust and safe solution by monitoring maritime environment where energy resources are critical. Energy constraints must be solved to enable continuous data collection and communication for environmental monitoring and surveillance so they can last. Their energy limitations and battery replacement difficulties can be addressed with a path planning approach.This paper considers the energy-aware path planning problem with autonomous underwater vehicles by five commonly used approaches, namely, Ant Colony Optimization-based Approach, Particle Swarm Optimization-based Approach, Teaching Learning-based Optimization-based Approach, Genetic Algorithm-based Approach, Grey Wolf Optimizer-based Approach. Simulations show that the system converges faster and performs better with genetic algorithm than the others. This paper also considers the case where direct traveling paths between some node pairs should be avoided due to several reasons including underwater flows, too narrow places for travel, and other risks like changing temperature and pressure. To tackle this case, we propose a modified genetic algorithm, the Safety-Aware Genetic Algorithm-based Approach, that blocks the direct paths between those nodes. In this scenario, the Safety-Aware Genetic Algorithm-based approach provides just a 3% longer path than the Genetic Algorithm-based approach which is the best approach among all these approaches. This shows that the Safety-Aware Genetic Algorithm-based approach performs very robustly. With our proposed robust and energy-efficient path-planning algorithms, the data gathered by sensors can be collected very quickly with much less energy, which enables the monitoring system to respond faster for ship accidents. It also reduces total energy consumption of sensors by communicating them closely and so extends the network lifetime.  a1989-1660