2025_Random Early Detection (Red) Based Algorithms For Solving The Congestion Problem In Vanets
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| country | Malaysia |
| date | 2025-06-17 15:20 |
| format | General Document |
| id | 17256 |
| institution | UniSZA |
| originalfilename | 17256_9cc3a4a48b825aa.pdf |
| person | Brian |
| recordtype | oai_dc |
| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=17256 |
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| spelling | 17256 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=17256 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 General Document Malaysia Library Staff (Top Management) Library Staff (Management) Library Staff (Support) Terengganu Faculty of Informatics & Computing English application/pdf 1.7 152 Server storage Scanned document UniSZA Private Access UniSZA Copyright©PWB2025 Microsoft® Word 2021 Network Traffic Management UniSZA Dissertations-Academic Random Early Detection (RED) Congestion Control Vehicular Ad-hoc Networks (VANETs) Packet loss Queue Management Wireless Communication Intelligent Transportation Systems Routing Algorithms Network Performance Optimization Delay Reduction Throughput Enhancement Vehicular ad Hoc Networks Wireless Communication Systems Computer Network Performance Traffic flow—Data processing 2025_Random Early Detection (Red) Based Algorithms For Solving The Congestion Problem In Vanets Introduction: Vehicular Ad Hoc Networks (VANETs) are essential to Intelligent Transportation Systems (ITS), facilitating seamless communication between vehicles and strategically deployed roadside units. These networks provide a robust alternative to conventional transportation systems, improving safety and efficiency. However, managing congestion is a critical challenge due to VANETs' dynamic mobility, intermittent connectivity, and fluctuating traffic density. These factors lead to packet loss, increased latency, and degraded Quality of Service (QoS). Existing congestion control strategies, such as Random Early Detection (RED), often struggle to adapt to these rapidly changing network conditions, resulting in delayed congestion detection, high packet drop rates, and performance instability. Therefore, there is a pressing need to optimize congestion management for VANETs. Methodology: This study proposes two enhanced algorithms: Mobile Random Early Detection (MRED) and Neighbor Random Early Detection (NRED), which address the shortcomings of the traditional RED mechanism. While RED determines packet drops based on static thresholds, it often underperforms in scenarios with high node mobility and varying traffic patterns. The static nature of RED leads to delayed responses, frequent packet drops, and reduced throughput. To overcome these limitations, the proposed algorithms dynamically adjust queue management parameters, improving congestion detection and packet handling. MRED incorporates mobility-aware features to adapt to vehicles' rapid movements, ensuring timely congestion control, while NRED focuses on managing local congestion by enhancing communication between neighboring nodes. A node-based throughput (NBTH) method is also introduced to assess network performance, considering varying node densities and traffic conditions. Simulation and real-world testing were employed to evaluate the algorithms using metrics such as throughput, end-to-end delay, and packet loss. Results: The findings demonstrate that MRED achieves a 3% improvement in network throughput compared to NRED, confirming its superior performance in handling congestion. The enhanced MRED algorithm ensures faster congestion detection, reduced packet loss, and more efficient resource utilization. In contrast to standard RED, which struggles with queue instability under fluctuating conditions, MRED maintains stable network performance by dynamically optimizing queue lengths and reducing computational complexity. The results align with initial expectations and validate the null hypothesis, proving that MRED outperforms not only NRED but also other state-of-the-art congestion control methods. Conclusion: This study emphasizes the importance of optimizing RED-based congestion control strategies to address VANETs' unique challenges. By combining MRED and NRED, the proposed solution enhances network performance, minimizes packet loss, and accelerates congestion detection. Unlike traditional RED, which relies on static thresholds and is prone to performance degradation, the dynamic nature of MRED ensures better adaptation to real-time network changes. Future research will explore the deployment of adaptive algorithms at network gateways, which can dynamically adjust queue management policies in response to traffic conditions. This approach promises to further enhance traffic management, reduce latency, and improve overall network reliability, ensuring that VANETs meet the demands of modern transportation systems. Brian 2025-06-17 15:20 uuid:9E31137F-7FD8-45DA-90FD-717F2E80E212 17256_9cc3a4a48b825aa.pdf Thesis |
| spellingShingle | 2025_Random Early Detection (Red) Based Algorithms For Solving The Congestion Problem In Vanets |
| state | Terengganu |
| subject | Dissertations-Academic Vehicular ad Hoc Networks Wireless Communication Systems Computer Network Performance Traffic flow—Data processing |
| summary | Introduction: Vehicular Ad Hoc Networks (VANETs) are essential to Intelligent Transportation Systems (ITS), facilitating seamless communication between vehicles and strategically deployed roadside units. These networks provide a robust alternative to conventional transportation systems, improving safety and efficiency. However, managing congestion is a critical challenge due to VANETs' dynamic mobility, intermittent connectivity, and fluctuating traffic density. These factors lead to packet loss, increased latency, and degraded Quality of Service (QoS). Existing congestion control strategies, such as Random Early Detection (RED), often struggle to adapt to these rapidly changing network conditions, resulting in delayed congestion detection, high packet drop rates, and performance instability. Therefore, there is a pressing need to optimize congestion management for VANETs. Methodology: This study proposes two enhanced algorithms: Mobile Random Early Detection (MRED) and Neighbor Random Early Detection (NRED), which address the shortcomings of the traditional RED mechanism. While RED determines packet drops based on static thresholds, it often underperforms in scenarios with high node mobility and varying traffic patterns. The static nature of RED leads to delayed responses, frequent packet drops, and reduced throughput. To overcome these limitations, the proposed algorithms dynamically adjust queue management parameters, improving congestion detection and packet handling. MRED incorporates mobility-aware features to adapt to vehicles' rapid movements, ensuring timely congestion control, while NRED focuses on managing local congestion by enhancing communication between neighboring nodes. A node-based throughput (NBTH) method is also introduced to assess network performance, considering varying node densities and traffic conditions. Simulation and real-world testing were employed to evaluate the algorithms using metrics such as throughput, end-to-end delay, and packet loss. Results: The findings demonstrate that MRED achieves a 3% improvement in network throughput compared to NRED, confirming its superior performance in handling congestion. The enhanced MRED algorithm ensures faster congestion detection, reduced packet loss, and more efficient resource utilization. In contrast to standard RED, which struggles with queue instability under fluctuating conditions, MRED maintains stable network performance by dynamically optimizing queue lengths and reducing computational complexity. The results align with initial expectations and validate the null hypothesis, proving that MRED outperforms not only NRED but also other state-of-the-art congestion control methods. Conclusion: This study emphasizes the importance of optimizing RED-based congestion control strategies to address VANETs' unique challenges. By combining MRED and NRED, the proposed solution enhances network performance, minimizes packet loss, and accelerates congestion detection. Unlike traditional RED, which relies on static thresholds and is prone to performance degradation, the dynamic nature of MRED ensures better adaptation to real-time network changes. Future research will explore the deployment of adaptive algorithms at network gateways, which can dynamically adjust queue management policies in response to traffic conditions. This approach promises to further enhance traffic management, reduce latency, and improve overall network reliability, ensuring that VANETs meet the demands of modern transportation systems. |
| title | 2025_Random Early Detection (Red) Based Algorithms For Solving The Congestion Problem In Vanets |
| title_full | 2025_Random Early Detection (Red) Based Algorithms For Solving The Congestion Problem In Vanets |
| title_fullStr | 2025_Random Early Detection (Red) Based Algorithms For Solving The Congestion Problem In Vanets |
| title_full_unstemmed | 2025_Random Early Detection (Red) Based Algorithms For Solving The Congestion Problem In Vanets |
| title_short | 2025_Random Early Detection (Red) Based Algorithms For Solving The Congestion Problem In Vanets |
| title_sort | 2025_random early detection (red) based algorithms for solving the congestion problem in vanets |