Adaptive Broadcast Scheme with Fuzzy Logic and Reinforcement Learning Dynamic Membership Functions in Mobile Ad Hoc Networks
Broadcasting in Mobile Ad Hoc Networks (MANETs) is significantly challenged by dynamic network topologies. Traditional fuzzy logic-based schemes that often rely on static fuzzy tables and fixed membership functions are limiting their ability to adapt to evolving network conditions. To address these...
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2025-07-01
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| author | Akobir Ismatov BeomKyu Suh Jian Kim YongBeom Park Ki-Il Kim |
| author_facet | Akobir Ismatov BeomKyu Suh Jian Kim YongBeom Park Ki-Il Kim |
| author_sort | Akobir Ismatov |
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| description | Broadcasting in Mobile Ad Hoc Networks (MANETs) is significantly challenged by dynamic network topologies. Traditional fuzzy logic-based schemes that often rely on static fuzzy tables and fixed membership functions are limiting their ability to adapt to evolving network conditions. To address these limitations, in this paper, we conduct a comparative study of two innovative broadcasting schemes that enhance adaptability through dynamic fuzzy logic membership functions for the broadcasting problem. The first approach (Model A) dynamically adjusts membership functions based on changing network parameters and fine-tunes the broadcast (BC) versus do-not-broadcast (DNB) ratio. Model B, on the other hand, introduces a multi-profile switching mechanism that selects among distinct fuzzy parameter sets optimized for various macro-level scenarios, such as energy constraints or node density, without altering the broadcasting ratio. Reinforcement learning (RL) is employed in both models: in Model A for BC/DNB ratio optimization, and in Model B for action decisions within selected profiles. Unlike prior fuzzy logic or reinforcement learning approaches that rely on fixed profiles or static parameter sets, our work introduces adaptability at both the membership function and profile selection levels, significantly improving broadcasting efficiency and flexibility across diverse MANET conditions. Comprehensive simulations demonstrate that both proposed schemes significantly reduce redundant broadcasts and collisions, leading to lower network overhead and improved message delivery reliability compared to traditional static methods. Specifically, our models achieve consistent packet delivery ratios (PDRs), reduce end-to-end Delay by approximately 23–27%, and lower Redundancy and Overhead by 40–60% and 40–50%, respectively, in high-density and high-mobility scenarios. Furthermore, this comparative analysis highlights the strengths and trade-offs between reinforcement learning-driven broadcasting ratio optimization (Model A) and parameter-based dynamic membership function adaptation (Model B), providing valuable insights for optimizing broadcasting strategies. |
| format | Article |
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| language | English |
| publishDate | 2025-07-01 |
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| series | Mathematics |
| spelling | doaj-art-ef9fcbdd4f9c487d81ad9e87f16460e22025-08-20T04:00:55ZengMDPI AGMathematics2227-73902025-07-011315236710.3390/math13152367Adaptive Broadcast Scheme with Fuzzy Logic and Reinforcement Learning Dynamic Membership Functions in Mobile Ad Hoc NetworksAkobir Ismatov0BeomKyu Suh1Jian Kim2YongBeom Park3Ki-Il Kim4Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Republic of KoreaDepartment of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Republic of KoreaDepartment of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Republic of KoreaDepartment of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Republic of KoreaDepartment of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Republic of KoreaBroadcasting in Mobile Ad Hoc Networks (MANETs) is significantly challenged by dynamic network topologies. Traditional fuzzy logic-based schemes that often rely on static fuzzy tables and fixed membership functions are limiting their ability to adapt to evolving network conditions. To address these limitations, in this paper, we conduct a comparative study of two innovative broadcasting schemes that enhance adaptability through dynamic fuzzy logic membership functions for the broadcasting problem. The first approach (Model A) dynamically adjusts membership functions based on changing network parameters and fine-tunes the broadcast (BC) versus do-not-broadcast (DNB) ratio. Model B, on the other hand, introduces a multi-profile switching mechanism that selects among distinct fuzzy parameter sets optimized for various macro-level scenarios, such as energy constraints or node density, without altering the broadcasting ratio. Reinforcement learning (RL) is employed in both models: in Model A for BC/DNB ratio optimization, and in Model B for action decisions within selected profiles. Unlike prior fuzzy logic or reinforcement learning approaches that rely on fixed profiles or static parameter sets, our work introduces adaptability at both the membership function and profile selection levels, significantly improving broadcasting efficiency and flexibility across diverse MANET conditions. Comprehensive simulations demonstrate that both proposed schemes significantly reduce redundant broadcasts and collisions, leading to lower network overhead and improved message delivery reliability compared to traditional static methods. Specifically, our models achieve consistent packet delivery ratios (PDRs), reduce end-to-end Delay by approximately 23–27%, and lower Redundancy and Overhead by 40–60% and 40–50%, respectively, in high-density and high-mobility scenarios. Furthermore, this comparative analysis highlights the strengths and trade-offs between reinforcement learning-driven broadcasting ratio optimization (Model A) and parameter-based dynamic membership function adaptation (Model B), providing valuable insights for optimizing broadcasting strategies.https://www.mdpi.com/2227-7390/13/15/2367MANETfuzzy logicreinforcement learningmembership function |
| spellingShingle | Akobir Ismatov BeomKyu Suh Jian Kim YongBeom Park Ki-Il Kim Adaptive Broadcast Scheme with Fuzzy Logic and Reinforcement Learning Dynamic Membership Functions in Mobile Ad Hoc Networks Mathematics MANET fuzzy logic reinforcement learning membership function |
| title | Adaptive Broadcast Scheme with Fuzzy Logic and Reinforcement Learning Dynamic Membership Functions in Mobile Ad Hoc Networks |
| title_full | Adaptive Broadcast Scheme with Fuzzy Logic and Reinforcement Learning Dynamic Membership Functions in Mobile Ad Hoc Networks |
| title_fullStr | Adaptive Broadcast Scheme with Fuzzy Logic and Reinforcement Learning Dynamic Membership Functions in Mobile Ad Hoc Networks |
| title_full_unstemmed | Adaptive Broadcast Scheme with Fuzzy Logic and Reinforcement Learning Dynamic Membership Functions in Mobile Ad Hoc Networks |
| title_short | Adaptive Broadcast Scheme with Fuzzy Logic and Reinforcement Learning Dynamic Membership Functions in Mobile Ad Hoc Networks |
| title_sort | adaptive broadcast scheme with fuzzy logic and reinforcement learning dynamic membership functions in mobile ad hoc networks |
| topic | MANET fuzzy logic reinforcement learning membership function |
| url | https://www.mdpi.com/2227-7390/13/15/2367 |
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