人工智能技术在水声网络路由协议中的应用探索

赵矣昊; 陈友淦; 李姜辉; 万磊; 陶毅; 王栩琛; 董妍函; 涂申奥; 许肖梅

doi:10.11999/JEIT250110

人工智能技术在水声网络路由协议中的应用探索

doi: 10.11999/JEIT250110 cstr: 32379.14.JEIT250110

赵矣昊^{1, 2},
陈友淦^{1, 2, 3, 5, ,},
李姜辉^{1, 2},
万磊^{1, 4},
陶毅^{1, 2},
王栩琛^{1, 2},
董妍函^{1, 2},
涂申奥^{1, 2},
许肖梅^{1, 2}

1.
厦门大学水声通信与海洋信息技术教育部重点实验室厦门 361005
2.
厦门大学海洋与地球学院厦门 361102
3.
厦门大学深圳研究院深圳 518000
4.
厦门大学信息学院厦门 361102
5.
鹭江创新实验室厦门 361005

基金项目: 国家自然科学基金(62271423, 62171394)，深圳市科技计划基础研究面上项目(JCYJ20230807091406013)

详细信息

作者简介:
赵矣昊：男，博士生，研究方向为水声通信及组网技术

陈友淦：男，教授，研究方向为水声通信及组网技术

李姜辉：男，教授，研究方向为水声通信、离岸碳捕集利用与存储

万磊：男，副教授，研究方向为水声通信与信号处理

陶毅：男，助理教授，研究方向为水声信号处理

王栩琛：男，博士生，研究方向为水下噪声处理

董妍函：女，博士生，研究方向为水声数据安全传输技术

涂申奥：男，博士生，研究方向为水声网络智能组网技术

许肖梅：女，教授，研究方向为水声遥测遥控技术

通讯作者:
陈友淦　chenyougan@xmu.edu.cn

中图分类号: TN929.3
计量
- 文章访问数: 396
- HTML全文浏览量: 156
- PDF下载量: 70
- 被引次数: 0
出版历程
- 收稿日期: 2025-02-25
- 修回日期: 2025-05-02
- 网络出版日期: 2025-05-20
- 刊出日期: 2025-08-27

Exploration of Application of Artificial Intelligence Technology in Underwater Acoustic Network Routing Protocols

ZHAO Yihao^{1, 2},
CHEN Yougan^{1, 2, 3, 5
, ,},
LI Jianghui^{1, 2},
WAN Lei^{1, 4},
TAO Yi^{1, 2},
WANG Xuchen^{1, 2},
DONG Yanhan^{1, 2},
TU Shen’ao^{1, 2},
XU Xiaomei^{1, 2}

1.
Key Laboratory of Underwater Acoustic Communication and Marine Information Technology (Xiamen University), Ministry of Education, Xiamen 361005, China
2.
College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
3.
Shenzhen Research Institute of Xiamen University, Shenzhen 518000, China
4.
School of Informatics, Xiamen University, Xiamen 361102, China
5.
Fujian Ocean Innovation Center, Xiamen 361005, China

Funds: The National Natural Science Foundation of China (62271423, 62171394), The Basic Research Program of Science and Technology of Shenzhen, China (JCYJ20230807091406013)

摘要

摘要: 随着海洋强国战略的发展，我国对海洋资源勘探、生态环境监测、军事安全应用等领域的海洋信息获取和数据传输需求迅速增加。水声网络作为水下数据传输的重要手段，其性能直接受到路由协议的影响。传统水声网络路由协议面临着动态海洋环境、节点能量有限以及网络安全等诸多挑战。近年来，人工智能技术凭借其强大的学习能力、数据洞察能力和适应性，逐渐被引入到水声网络路由协议中。该文综述了国内外人工智能技术在水声网络路由协议中的应用研究进展，详细分析了其在平面路由和层级路由中的应用情况。研究结果表明，人工智能技术能够有效优化路由决策，降低能耗，减少端到端时延，并在一定程度上提升网络安全性能。然而，当前的研究仍主要基于仿真，且在算法复杂度评估和硬件实现方面存在不足。未来的研究方向应包括开发更贴近实际海洋环境的仿真平台，进行海试实验以验证算法性能，同时降低人工智能算法的复杂度，以适应水声节点的硬件条件。该文旨在为水声网络路由协议中应用人工智能技术提供参考，并对未来研究方向提出建议。
- 人工智能 /
- 水声通信 /
- 水声网络 /
- 路由协议
Abstract: Significance In response to the strategic emphasis on maritime power, China has experienced growing demand for ocean resource exploration, ecological monitoring, and defense applications. Underwater acoustic networks provide an effective solution for data acquisition in these domains, with network performance largely dependent on the design and implementation of routing protocols. These protocols determine the transmission path and method, forming a foundation for optimizing underwater communication. Recent advances in Artificial Intelligence (AI) have prompted efforts to apply AI techniques to underwater acoustic network routing. By leveraging AI’s learning capacity, data insight capability, and adaptability, researchers aim to address challenges posed by dynamic underwater environments, energy limitations of nodes, and potential security threats. This paper examines the integration of AI technology into underwater acoustic network routing protocols and provides a critical evaluation of current research progress. Progress This paper reviews the application of AI technology in underwater acoustic network routing protocols, classifying existing approaches into flat and hierarchical routing categories. In flat routing, AI methods such as conventional heuristic algorithms, reinforcement learning, and deep learning have been applied to improve routing decisions. For hierarchical routing, AI is utilized not only for routing optimization but also for node clustering and layer structuring. These applications offer potential benefits, including enhanced routing efficiency, reduced energy consumption, improved end-to-end delay, and strengthened network security. Most performance evaluations are based on simulations. However, simulation environments vary considerably across studies, particularly in node quantity and density, ranging from small-scale to very large-scale networks. This variability complicates quantitative comparisons of performance metrics. Additionally, replicating these simulation scenarios in sea trials is limited by the logistical and financial constraints of deploying and recovering large numbers of nodes, thus impeding the validation of protocol performance under real-world conditions. The review further identifies critical challenges in applying AI to underwater acoustic networks. Many AI-based protocols operate under impractical assumptions, such as global knowledge of node positions and energy levels, which is rarely achievable in dynamic underwater settings. Maintaining such information requires substantial communication overhead, thereby increasing energy consumption and delay. Furthermore, the computational complexity of AI algorithms—particularly deep learning models—presents difficulties for implementation on underwater nodes with limited power, processing, and storage capacities. Few studies provide detailed complexity analyses, and hardware-based performance verifications remain scarce. This lack of real-world validation limits the assessment of the practical feasibility and effectiveness of AI-enabled routing protocols. Conclusions AI technology offers considerable potential for enhancing underwater acoustic network routing protocols by addressing key challenges such as environmental variability, energy constraints, and security threats. However, current research is constrained by several limitations. Many studies rely on unrealistic assumptions regarding the availability of complete node information, which is impractical in dynamic underwater settings. The acquisition and maintenance of such information entail substantial communication overhead, leading to increased energy consumption and delay. Moreover, the computational demands of AI algorithms—particularly deep learning models—often exceed the capabilities of resource-limited underwater nodes. Performance assessments remain predominantly simulation-based, with limited hardware implementation, thereby restricting the verification of real-world feasibility and effectiveness. Prospects Future research should prioritize the development of more accurate and realistic simulation platforms to support the evaluation of AI-based routing protocols. This includes the integration of advanced channel models and real-world observational data to improve simulation fidelity. Establishing standardized simulation conditions will also be essential for enabling consistent performance comparisons across studies. In parallel, greater emphasis should be placed on hardware implementation of AI algorithms, with efforts directed toward reducing algorithmic complexity and storage demands to accommodate the limitations of energy-constrained underwater nodes. Exploring cost-effective validation approaches, such as small-scale sea trials and semi-physical simulation frameworks, will be critical for assessing the practical performance and deployment feasibility of AI-enabled routing protocols.
- Artificial Intelligence (AI) /
- Underwater acoustic communication /
- Underwater acoustic network /
- Routing protocol

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图 1 水声网络应用场景示意

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图 2 水声网络层次划分

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图 3 影响水声网络节点间通信的因素

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图 4 人工智能技术构造图示

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图 5 QL算法选择最优中继示意图

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图 6 水声网络路由结构示意图

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图 7 基于人工智能的平面路由和层级路由仿真验证中网络规模情况统计图

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表 1 人工智能技术在水声网络平面路由协议中的应用探索

文献索引	算法类别	算法	年份	优化目标				验证方法	节点个数	网络范围
文献索引	算法类别	算法	年份	可靠性和可扩展性	能耗	端到端时延	网络安全性	验证方法	节点个数	网络范围
[27]	常规智能算法	ACO&AFS	2020	√	√	√	×	仿真	20	14×5 km²
[28]		ACO	2021	√	√	√	×	仿真	18	12×5 km²
[29]			2023	√	√	√	×	仿真	250	0.5×0.5×0.5 km³
[30]			2023	√	√	√	×	仿真	15～50	5×5×3 km³
[31]		MO-CBACO	2023	√	√	√	√	仿真	100～500	1×1 km²
[32]		CSO	2020	√	√	×	×	仿真	150～450	1.5×1.5×1.5 km³
[33]		博弈论	2020	√	√	√	×	仿真	200～400	0.5×0.5×0.5 km³
[34]		模糊控制	2021	√	√	√	×	仿真&湖试	仿真300–800&湖试5	仿真0.5×0.5×0.5 km³
[35]		GA&SA &GSS	2022	√	√	×	√	仿真	10～40	–
[36]		SVM	2023	√	√	√	×	仿真	100～500	0.5×0.5×0.5 km³
[37]		GA&PSO	2024	√	√	√	×	仿真	80～160	1×1×1 km³
[38]	强化学习算法	RL	2019	√	√	√	×	仿真&海试	仿真6～40&海试6	仿真4×4×0.24 km³
[18]		QL	2010	√	√	×	×	仿真	250	0.5×0.5×0.5 km³
[39]			2019	√	√	√	×	仿真	100～300	5×5×2.5 km³
[40]			2020	√	√	√	×	仿真	200～800	0.5×0.5×0.5 km³
[41]			2021	√	√	√	×	仿真	100～500	0.5×0.5×0.5 km³
[42]			2021	√	√	×	×	仿真	100	–
[43]			2021	√	√	×	×	仿真	18	14×5 km²
[44]			2021	√	√	√	×	仿真	50～600	0.5×0.5×0.5 km³
[45]			2022	√	√	√	×	仿真	100～500	4×4×5 km³
[46]			2023	√	√	√	×	仿真	10～40	6×6 km²
[47]			2023	√	√	√	×	仿真	40～90	4×4×4 km³
[48]			2024	√	×	×	√	仿真	100～500	0.5×0.5×0.5 km³
[49]			2024	√	√	√	×	仿真	40～90	4×4×4 km³
[50]	深度学习与深度强化学习	DNN	2021	√	√	√	×	仿真	30	1.5×1.5 km²
[51]		BP-NN	2023	√	√	×	×	仿真	60	1×1 km²
[52]		GNN	2024	√	√	√	×	仿真	50～250	5×5×3 km³
[53]		GAN&QL	2024	√	√	√	√	仿真	100	0.5×0.5×0.5 km³
[54]		DQN	2019	√	√	√	×	仿真	80	5×4×4.5 km³
[55]			2021	√	√	√	×	仿真	100	0.5×0.4×0.45 km³
[56]			2022	√	√	×	×	仿真	500～3 000	1×1×0.5 km³
[57]			2023	√	√	√	×	仿真	10～50	0.6×0.6×0.5 km³

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表 2 人工智能技术在水声网络层级路由协议中的应用探索

文献索引	算法作用	算法	年份	优化目标				验证方法	节点个数	网络范围
文献索引	算法作用	算法	年份	可靠性和可扩展性	能耗	端到端时延	网络安全性	验证方法	节点个数	网络范围
[59]	仅层级划分	K-Means	2020	√	√	×	×	仿真	100	5×5 km²
[60]			2021	√	√	×	×	仿真	200	0.02×0.02 km²
[61]			2022	√	√	×	×	仿真	100	0.1×0.1×0.1 km³
[62]		K-Means &QL	2021	√	√	×	×	仿真	60	12.5×4 km²
[64]		Birch	2021	√	√	×	×	仿真	15000	0.8×0.8×0.8 km³
[65]		MFO	2019	√	√	×	×	仿真	40 (80)	0.5×0.5×0.5 km³ (2×2×2 km³)
[66]		模糊聚类&PSO	2019	√	√	×	×	仿真	100	0.1×0.1×0.1 km³
[67]		模糊聚类&PSO	2021	√	√	×	×	仿真	100	0.5×0.5×0.5 km³
[68]		DFO	2021	√	√	×	×	仿真	20～200	0.5×2 km²
[69]		博弈论	2021	√	√	×	×	仿真	100	0.1×0.1×0.1 km³
[70]		PSO	2022	√	√	×	×	仿真	100	0.1×0.1×0.1 km³
[71]		GSO	2023	√	√	×	×	仿真	20～200	0.5×2 km²
[72]		DFO	2023	√	√	×	×	仿真	100	0.2×0.2 km²
[73]	层级划分& 路由决策	GA	2018	√	√	√	×	仿真	350	1×1×0.1 km³
[74]		CSRO	2022	√	√	×	×	仿真	300	–
[75]		QL	2022	√	√	×	×	仿真	100	3×3×2.5 km³
[76]		QL	2023	√	√	√	×	仿真	250	0.5×0.5×0.5 km³
[77]		ChOA	2024	√	√	√	×	仿真	300	0.2×0.2 km²
[78]		CKHA& GSO	2022	√	√	√	√	仿真	300	2×2×2 km³
[79]		EPO&GOA	2022	√	√	√	×	仿真	400	–
[80]	仅路由决策	ACO	2020	√	√	√	×	仿真	300～500	5×5×1 km³
[81]		GA	2020	√	√	√	×	仿真	200～300	5×5×1 km³
[82]		BOA	2022	√	√	√	×	仿真	150～450	0.5×0.5×0.5 km³
[83]		QL	2021	√	√	√	×	仿真	170	0.25×0.25×0.08 km³
[84]		QL	2022	√	√	√	×	仿真	100～500	5×5×5 km³
[85]		DBN	2021	√	√	×	×	仿真	200	0.5×0.5×0.5 km³
[86]		GMM-HMM-LSTM&PSO	2024	√	√	√	√	仿真	50-200	2×2×1.5 km³

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