Underwater Acoustic MAC Protocol for Multi-Objective Optimization Based on Multi-Agent Reinforcement Learning
In underwater acoustic networks (UANs), communication between nodes is susceptible to long propagation delays, limited energy, and channel conflicts, and traditional multi-access control (MAC) protocols cannot easily cope with these challenges. To enhance network throughput and balance channel alloc...
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MDPI AG
2025-02-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/2/123 |
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| author | Jinfang Jiang Yiling Dong Guangjie Han Gang Su |
| author_facet | Jinfang Jiang Yiling Dong Guangjie Han Gang Su |
| author_sort | Jinfang Jiang |
| collection | DOAJ |
| description | In underwater acoustic networks (UANs), communication between nodes is susceptible to long propagation delays, limited energy, and channel conflicts, and traditional multi-access control (MAC) protocols cannot easily cope with these challenges. To enhance network throughput and balance channel allocation fairness and energy efficiency, this paper proposes a multi-objective optimization MAC protocol (MOMA-MAC) based on multi-agent reinforcement learning. MOMA-MAC utilizes a delay reward mechanism combined with the Multi-agent Proximal Policy Optimization Algorithm (MAPPO) to design a dual reward mechanism, which enables agents to adaptively collaborate and compete to optimize the use of network resources. According to experimental results, MOMA-MAC performs noticeably better than traditional MAC protocols and deep reinforcement learning-based methods in terms of throughput, energy efficiency, and fairness in multi-agent scenarios, showing great potential for improving communication efficiency and energy utilization. |
| format | Article |
| id | doaj-art-09c9f2aa445b49e6acd0f1fced3934cb |
| institution | DOAJ |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-09c9f2aa445b49e6acd0f1fced3934cb2025-08-20T02:44:46ZengMDPI AGDrones2504-446X2025-02-019212310.3390/drones9020123Underwater Acoustic MAC Protocol for Multi-Objective Optimization Based on Multi-Agent Reinforcement LearningJinfang Jiang0Yiling Dong1Guangjie Han2Gang Su3Key Laboratory of Maritime Intelligent Cyberspace Technology, Hohai University, Ministry of Education, Changzhou 213200, ChinaKey Laboratory of Maritime Intelligent Cyberspace Technology, Hohai University, Ministry of Education, Changzhou 213200, ChinaKey Laboratory of Maritime Intelligent Cyberspace Technology, Hohai University, Ministry of Education, Changzhou 213200, ChinaKey Laboratory of Maritime Intelligent Cyberspace Technology, Hohai University, Ministry of Education, Changzhou 213200, ChinaIn underwater acoustic networks (UANs), communication between nodes is susceptible to long propagation delays, limited energy, and channel conflicts, and traditional multi-access control (MAC) protocols cannot easily cope with these challenges. To enhance network throughput and balance channel allocation fairness and energy efficiency, this paper proposes a multi-objective optimization MAC protocol (MOMA-MAC) based on multi-agent reinforcement learning. MOMA-MAC utilizes a delay reward mechanism combined with the Multi-agent Proximal Policy Optimization Algorithm (MAPPO) to design a dual reward mechanism, which enables agents to adaptively collaborate and compete to optimize the use of network resources. According to experimental results, MOMA-MAC performs noticeably better than traditional MAC protocols and deep reinforcement learning-based methods in terms of throughput, energy efficiency, and fairness in multi-agent scenarios, showing great potential for improving communication efficiency and energy utilization.https://www.mdpi.com/2504-446X/9/2/123underwater acoustic networksmulti-access control protocolmulti-objective optimization |
| spellingShingle | Jinfang Jiang Yiling Dong Guangjie Han Gang Su Underwater Acoustic MAC Protocol for Multi-Objective Optimization Based on Multi-Agent Reinforcement Learning Drones underwater acoustic networks multi-access control protocol multi-objective optimization |
| title | Underwater Acoustic MAC Protocol for Multi-Objective Optimization Based on Multi-Agent Reinforcement Learning |
| title_full | Underwater Acoustic MAC Protocol for Multi-Objective Optimization Based on Multi-Agent Reinforcement Learning |
| title_fullStr | Underwater Acoustic MAC Protocol for Multi-Objective Optimization Based on Multi-Agent Reinforcement Learning |
| title_full_unstemmed | Underwater Acoustic MAC Protocol for Multi-Objective Optimization Based on Multi-Agent Reinforcement Learning |
| title_short | Underwater Acoustic MAC Protocol for Multi-Objective Optimization Based on Multi-Agent Reinforcement Learning |
| title_sort | underwater acoustic mac protocol for multi objective optimization based on multi agent reinforcement learning |
| topic | underwater acoustic networks multi-access control protocol multi-objective optimization |
| url | https://www.mdpi.com/2504-446X/9/2/123 |
| work_keys_str_mv | AT jinfangjiang underwateracousticmacprotocolformultiobjectiveoptimizationbasedonmultiagentreinforcementlearning AT yilingdong underwateracousticmacprotocolformultiobjectiveoptimizationbasedonmultiagentreinforcementlearning AT guangjiehan underwateracousticmacprotocolformultiobjectiveoptimizationbasedonmultiagentreinforcementlearning AT gangsu underwateracousticmacprotocolformultiobjectiveoptimizationbasedonmultiagentreinforcementlearning |