## 2 Related Works  
Path planning for AUV in the IoUT environment plays a significant role in improving data collection, conserving energy, and increasing system reliability. Many approaches have been explored to improve AUV navigation, ranging from heuristics to nature-inspired algorithms. In the IoUT environment, AUV path planning encounters many challenges, including real-time adaptation, computational complexity, energy consumption, and fluctuations in the aquatic environment. This survey presents a review of ten research papers, arranged chronologically, focusing on the approaches and challenges they face. In [6], the authors utilized evolutionary algorithms to optimize paths in complex environments while improving mobility; however, the biggest challenge lies in the real-time computational load. In [7], the authors explored energy-saving strategies to extend the mission duration of autonomous vehicles while maintaining the quality of data collection. The biggest challenge of this approach is to balance energy efficiency and data accuracy. In [8], the authors developed a full-coverage path planning algorithm to ensure the comprehensive scanning of underwater target areas, despite the difficulty in adapting to dynamic terrain. The authors in [9] presented a dynamic detection algorithm to adjust autonomous vehicle trajectories in real time, which improves energy efficiency; however, this technique faces the challenges of environmental fluctuations. The authors in [10] investigated sensor clustering protocols to optimize autonomous vehicle trajectories, reducing energy consumption and increasing network lifetime, but data accuracy is considered a limitation. The authors in [11] presented a path optimization framework using synchronous wireless information and power transmission (SWIPT) for autonomous vehicles, which achieves a balance between data collection and energy harvesting; nevertheless, achieving data accuracy while saving energy is a challenge. The authors in [12] proposed the use of neural networks inspired by nature to guide autonomous underwater vehicles during path planning in complex environments, which improves both coverage and navigation efficiency. The authors in [13] presented a heuristic method for optimizing autonomous marine vehicle (AMV) trajectories, including the integration of environmental data such as ocean currents to improve coordination and reduce mission time. In [14], the authors applied evolutionary algorithms to improve coverage and reduce energy consumption in aquatic environments while improving efficiency. However, the computational complexity remains a major challenge. In [15], the authors explored the use of pre-defined routing points for autonomous underwater vehicle navigation, which reduces navigation time and improves efficiency. In [16], the study presented the solution to the path planning problem of UAVs to be like the solution to the traveling salesman problem (TSP). The article proposed an improved genetic algorithm and a hybrid algorithm based on particle swarm optimization together with ant colony optimization. In [17], the researchers proposed two algorithms to solve the problem of path planning for UAVs. The proposed techniques have previously been used for solving the previous optimization problems of Close-Enough Traveling Salesman and traveling salesman. In [18], the authors presented the approach of the traveling salesman problem to generate robust and quick motions for Mine Countermeasures in the presence of uncertain marine environmental conditions (vehicle dynamics, complex bathymetry, and ocean currents). The method and challenges in each related work are summarized in Table 1.  
Different from those of the previous works, the contributions of the method proposed in this paper are summarized as follows;  
* A boundary tour optimization algorithm is proposed to solve AUV battery-based trajectory planning for collecting sensor node information with minimum time and the energy consumption required in 3D environments.