RL-BMAC: An RL-Based MAC Protocol for Performance Optimization in Wireless Sensor Networks
Applications of wireless sensor networks have significantly increased in the modern era. These networks operate on a limited power supply in the form of batteries, which are normally difficult to replace on a frequent basis. In wireless sensor networks, sensor nodes alternate between sleep and activ...
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MDPI AG
2025-04-01
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| author | Owais Khan Sana Ullah Muzammil Khan Han-Chieh Chao |
| author_facet | Owais Khan Sana Ullah Muzammil Khan Han-Chieh Chao |
| author_sort | Owais Khan |
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| description | Applications of wireless sensor networks have significantly increased in the modern era. These networks operate on a limited power supply in the form of batteries, which are normally difficult to replace on a frequent basis. In wireless sensor networks, sensor nodes alternate between sleep and active states to conserve energy through different methods. Duty cycling is among the most commonly used methods. However, it suffers from problems like unnecessary idle listening, extra energy consumption, and packet drop rate. A Deep Reinforcement Learning-based B-MAC protocol called (RL-BMAC) has been proposed to address this issue. The proposed protocol deploys a deep reinforcement learning agent with fixed hyperparameters to optimize the duty cycling of the nodes. The reinforcement learning agent monitors essential parameters such as energy level, packet drop rate, neighboring nodes’ status, and preamble sampling. The agent stores the information as a representative state and adjusts the duty cycling of all nodes. The performance of RL-BMAC is compared to that of conventional B-MAC through extensive simulations. The results obtained from the simulations indicate that RL-BMAC outperforms B-MAC in terms of throughput by 58.5%, packet drop rate by 44.8%, energy efficiency by 35%, and latency by 26.93% |
| format | Article |
| id | doaj-art-eae382de0a594c76a02e0f20b6812c60 |
| institution | OA Journals |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-eae382de0a594c76a02e0f20b6812c602025-08-20T02:33:57ZengMDPI AGInformation2078-24892025-04-0116536910.3390/info16050369RL-BMAC: An RL-Based MAC Protocol for Performance Optimization in Wireless Sensor NetworksOwais Khan0Sana Ullah1Muzammil Khan2Han-Chieh Chao3Department of Computer and Software Technology, University of Swat, Swat 19120, PakistanDepartment of Computer and Software Technology, University of Swat, Swat 19120, PakistanDepartment of Computer and Software Technology, University of Swat, Swat 19120, PakistanDepartment of Applied Informatics, Fo Guang University, Yilan 262307, TaiwanApplications of wireless sensor networks have significantly increased in the modern era. These networks operate on a limited power supply in the form of batteries, which are normally difficult to replace on a frequent basis. In wireless sensor networks, sensor nodes alternate between sleep and active states to conserve energy through different methods. Duty cycling is among the most commonly used methods. However, it suffers from problems like unnecessary idle listening, extra energy consumption, and packet drop rate. A Deep Reinforcement Learning-based B-MAC protocol called (RL-BMAC) has been proposed to address this issue. The proposed protocol deploys a deep reinforcement learning agent with fixed hyperparameters to optimize the duty cycling of the nodes. The reinforcement learning agent monitors essential parameters such as energy level, packet drop rate, neighboring nodes’ status, and preamble sampling. The agent stores the information as a representative state and adjusts the duty cycling of all nodes. The performance of RL-BMAC is compared to that of conventional B-MAC through extensive simulations. The results obtained from the simulations indicate that RL-BMAC outperforms B-MAC in terms of throughput by 58.5%, packet drop rate by 44.8%, energy efficiency by 35%, and latency by 26.93%https://www.mdpi.com/2078-2489/16/5/369wireless sensor networksmachine learningenergy optimizationdeep reinforcement learningMAC protocols |
| spellingShingle | Owais Khan Sana Ullah Muzammil Khan Han-Chieh Chao RL-BMAC: An RL-Based MAC Protocol for Performance Optimization in Wireless Sensor Networks Information wireless sensor networks machine learning energy optimization deep reinforcement learning MAC protocols |
| title | RL-BMAC: An RL-Based MAC Protocol for Performance Optimization in Wireless Sensor Networks |
| title_full | RL-BMAC: An RL-Based MAC Protocol for Performance Optimization in Wireless Sensor Networks |
| title_fullStr | RL-BMAC: An RL-Based MAC Protocol for Performance Optimization in Wireless Sensor Networks |
| title_full_unstemmed | RL-BMAC: An RL-Based MAC Protocol for Performance Optimization in Wireless Sensor Networks |
| title_short | RL-BMAC: An RL-Based MAC Protocol for Performance Optimization in Wireless Sensor Networks |
| title_sort | rl bmac an rl based mac protocol for performance optimization in wireless sensor networks |
| topic | wireless sensor networks machine learning energy optimization deep reinforcement learning MAC protocols |
| url | https://www.mdpi.com/2078-2489/16/5/369 |
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