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<p>Vehicular ad hoc networks (VANETs) have emerged as a key area of interest in the research community due to their wide range of applications. As the number of vehicles increases, VANETs encounter challenges with access control, which affects communication between the source vehicle and other vehicles. To overcome this issue, the prioritization and meta-heuristic-based congestion control (PMCC) scheme is proposed in the given paper. It addresses this congestion by prioritizing messages and dynamically adjusting the transmission rate of safety messages. In the prioritization phase, message priority is determined using both dynamic and static factors. Then, to control channel load within VANETs, the PMCC scheme employs a Tabu Search optimization (TSO) algorithm to adjust the transmission rate of beacon messages. Comparative analysis of the PMCC scheme with existing schemes such as urban vehicular broadCAST (UV-CAST), distributed-fair power adjustment for the vehicular environment (D-FPAV), and loss differentiation rate adaptation (LORA) is conducted based on metrics including reception probability, average delay, and vehicle coverage ratio. The results show the superior performance of the proposed PMMC scheme in all aspects. </p>

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Prioritization and meta-heuristic approach for efficient congestion control in vehicular ad hoc networks (VANETs)

  • 期刊论文

  • 发表日期：

    2025年5月8日

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  • Swati Sharma,
  • Saba Khanum,
  • R. B. Madhumala,
  • Abhilash Maroju,
  • Saurabh Aggarwal

摘要

Vehicular ad hoc networks (VANETs) have emerged as a key area of interest in the research community due to their wide range of applications. As the number of vehicles increases, VANETs encounter challenges with access control, which affects communication between the source vehicle and other vehicles. To overcome this issue, the prioritization and meta-heuristic-based congestion control (PMCC) scheme is proposed in the given paper. It addresses this congestion by prioritizing messages and dynamically adjusting the transmission rate of safety messages. In the prioritization phase, message priority is determined using both dynamic and static factors. Then, to control channel load within VANETs, the PMCC scheme employs a Tabu Search optimization (TSO) algorithm to adjust the transmission rate of beacon messages. Comparative analysis of the PMCC scheme with existing schemes such as urban vehicular broadCAST (UV-CAST), distributed-fair power adjustment for the vehicular environment (D-FPAV), and loss differentiation rate adaptation (LORA) is conducted based on metrics including reception probability, average delay, and vehicle coverage ratio. The results show the superior performance of the proposed PMMC scheme in all aspects.