College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.
Sensors (Basel). 2023 Jan 4;23(2):578. doi: 10.3390/s23020578.
Autonomous underwater vehicles (AUVs)-assisted mobile data collection in underwater wireless sensor networks (UWSNs) has received significant attention because of their mobility and flexibility. To satisfy the increasing demand of diverse application requirements for underwater data collection, such as time-sensitive data freshness, emergency event security as well as energy efficiency, in this paper, we propose a novel multi-modal AUV-assisted data collection scheme which integrates both acoustic and optical technologies and takes advantage of their complementary strengths in terms of communication distance and data rate. In this scheme, we consider the age of information (AoI) of the data packet, node transmission energy as well as energy consumption of the AUV movement, and we make a trade-off between them to retrieve data in a timely and reliable manner. To optimize these, we leverage a deep reinforcement learning (DRL) approach to find the optimal motion trajectory of AUV by selecting the suitable communication options. In addition to that, we also design an optimal angle steering algorithm for AUV navigation under different communication scenarios to reduce energy consumption further. We conduct extensive simulations to verify the effectiveness of the proposed scheme, and the results show that the proposed scheme can significantly reduce the weighted sum of AoI as well as energy consumption.
自主水下机器人（AUV）辅助的水下无线传感器网络（UWSN）中的移动数据收集由于其移动性和灵活性而受到了广泛关注。为了满足水下数据收集的各种应用需求（如时间敏感的数据新鲜度、紧急事件安全性和能源效率）的不断增长的需求，在本文中，我们提出了一种新颖的多模态 AUV 辅助数据收集方案，该方案结合了声学和光学技术，并利用它们在通信距离和数据速率方面的互补优势。在该方案中，我们考虑了数据包的信息年龄（AoI）、节点传输能量以及 AUV 运动的能量消耗，并在它们之间进行权衡，以便及时可靠地检索数据。为了优化这些参数，我们利用深度强化学习（DRL）方法通过选择合适的通信选项来找到 AUV 的最佳运动轨迹。此外，我们还设计了一种针对不同通信场景的 AUV 导航的最佳角度转向算法，以进一步降低能量消耗。我们进行了广泛的模拟来验证所提出方案的有效性，结果表明，所提出的方案可以显著降低加权和的 AoI 以及能量消耗。
