海洋是沿海国家经济和社会可持续发展的重要保障，是影响国家战略安全的重要因素。但是由于水声信道与陆上无线信道有很大不同，我们不能直接将传统的无线传感网协议和优化机制直接运用在水下物联网（Internet of Underwater Things, IoUT）中。水下无线传感网在协议设计和移动节点部署等方面主要面临的挑战包括：（1）水下节点稀疏部署导致路由空洞问题；（2）水下移动节点导致网络拓扑变化；（3）无人潜航器（Autonomous Underwater Vehicle，AUV）的水下数据采集时效性受海水湍流和协同策略的影响。基于上述难题，本文设计面向水下场景的路由协议和无人潜航器协同的水下数据采集系统。 首先，论文介绍了国外主流水下信息采集系统。然后总结了主流的水下组网路由协议的分类和实现原理。接着，重点讨论了多水下无人潜航器协同的水下信息采集策略的最新研究进展，并且调研了水下数据传输的数据时效性问题。 其次，论文提出了一种基于Q-learning的蚁群路由协议(QLACO)，该协议使用奖励机制和人工蚂蚁来确定全局最优路由选择。QLACO使用奖励功能来适应动态的水下环境，提高数据包传递率（Packet Delivery Ratio，PDR）。仿真结果表明，相比基于深度信息转发路由协议（DBR）和高能效自适应路由协议（QELAR），所提出QLACO协议在能量均衡性和数据包递送率有着更好的性能。QLACO协议相比QELAR协议降低了28.2%的平均传输时延，并且提高了20.3%数据包递送率。 再次，论文设计了一种多AUV协同的异构水下信息收集方案。论文利用有限休假 M/G/1 排队系统建模多AUV数据传输过程。满足系统稳定性前提下，推导了AUV服务用户的最优上界和用户的稳态分布。然后设计了一种调整系统容量的低复杂度自适应算法。仿真结果表明，所提出策略相比单无人潜航器数据采集策略，能够降低23.1%峰值信息年龄和58.3%的总系统能耗。 最后，论文研究了海洋湍流对自主潜航器运动耦合关系，进而提出一种避免海洋湍流场影响的无人潜航器路径规划算法和协同信息采集策略。首先利用低时间复杂度的群智算法完成AUV的轨迹规划。然后基于李雅普诺夫优化算法，实现了水下节点的能量效率和水下无线传感网数据队列稳定性的联合优化。仿真结果表明，所提出方法相比贪婪策略提高37.5%系统能量效率，同时有效提升了网络稳定性。并且根据不同水下任务，随需调整联合优化策略。
The ocean is an important guarantee for the sustainable economic and social development of coastal countries, and it also affects national strategic security. Because underwater acoustic channel is different from wireless channels. Therefore, the wireless sensor network protocol and optimization mechanism on land cannot be directly used in the Internet of Underwater Things (IoUT) before modification. As one of the most challenging wireless channels, underwater wireless sensor networks (UWSNs) face huge challenges in protocol design and mobile node deployment: (1) Sparse deployment of underwater nodes leads to routing void problem; (2) Underwater mobility nodes lead to changes in network topology; (3) The timeliness of the Autonomous Underwater Vehicle (AUV) aided data collection system is affected by dynamic turbulence and node coordination strategies. For the sake of addressing above issues, this paper designs an underwater routing protocol and two AUV aided underwater data collection schemes.First, the paper introduces the practical underwater information collection system. Then, the realization principle and system model of the underwater routing protocols are investigated. Then, the key techniques involved in the cooperative networking mechanism of AUV are discussed, and the timeliness of underwater data transmission is analyzed in detail.Second, the paper proposes a Q-learning aided ant colony routing protocol (QLACO) to address the issues of energy-efficiency and link instability in UWSNs, which uses both the reward mechanism and artificial ants to determine a global optimal routing selection. QLACO uses the reward function to adapt to the dynamic underwater environment and enhance the packet delivery ratio (PDR). Simulation results show that, the proposed QLACO has better performance in energy balance and packet delivery rate than DBR and QELAR. Compared with the QELAR, QLACO reduces the average transmission delay by 28.2%, and the routing hole avoidance mechanism effectively improves the packet delivery rate by 20.3%.Thirdly, this paper designs a multi-AUV assisted heterogeneous underwater information collection scheme. Moreover, the limited service M/G/1 vacation queueing model is utilized to model the process of information exchange. A low-complexity adaptive algorithm for adjusting the upper limit of the queuing length is also proposed. The simulation results show that the proposed scheme reduces PAoI by 23.1% and the total system energy consumption by 58.3% compared with the Single AUV scheme.Finally, this work investigates the coupling relationship between underwater turbulence and the motion of autonomous underwater vehicle. Then, this paper proposes a heterogeneous AUV aided information collection system with the aim of maximizing the energy efficiency of IoUT nodes taking into account underwater turbulent field relying on a horizontal acoustic doppler current profiler（HADCP）. Moreover, based on the particle swarm optimization (PSO), we obtain the trajectory of AUVs with low time complexity. Additionally, a two-stage joint optimization algorithm based on Lyapunov optimization is constructed to strike a trade-off between energy efficiency and system queue backlog iteratively. The simulation results show that the proposed method improves the energy efficiency by 37.5% compared with the greedy strategy, and effectively improves the network stability. Moreover, the proposed algorithm can change its modes to adapt to the different underwater task.
