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Vehicular ad-hoc networks (VANETs) technology is emerging daily, becoming an important research topic in the academic research sector. This is for the reason that the types of communication in VANETs, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I), become highly suitable for intelligent transportation system (ITS)-oriented applications. Due to the individuality of VANETs, such as random and unpredictable mobility, high speed, stability, efficiency, and effectiveness of VANETs are reduced during the process of communication. An effective routing model is needed to overcome this problem. In this paper, mobility prediction in an advanced destination routing model (MACH-DAR) is proposed to improve the efficiency of the VANETs. This MACH-DAR approach is divided into three segments: Advanced CH (ACH) selection, ACH in destination-aware routing model (ACH-DAR), and mobility prediction in ACH-DAR. The proposed MACH-DAR is mainly used to improve the efficiency of VANETs through clustering, destination-aware transmission of inter-cluster and intra-cluster methods, and mobility prediction to avoid congestion. The parameters that are used for result calculation are energy efficiency, packet delivery ratio, routing overhead, and throughput. The earlier research used for the comparative analysis was CP-DCM and CR-DPBP. The evaluation of the results shows that the proposed MACH-DAR approach produced higher energy efficiency, packet delivery ratio, and throughput and lower routing overhead than the earlier works.

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Mobility Prediction and Destination-Aware Advanced Clustering-Based Vehicular Routing Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Abdalrahman Fatikhan Ataalla,
  • Karar Aljawaheri,
  • Ramy Riad Al-Fatlawy,
  • Mustafa Mohammed Mahmood,
  • Hassan Mohammed Abed,
  • Zahraa Saad Abdulali

摘要

Vehicular ad-hoc networks (VANETs) technology is emerging daily, becoming an important research topic in the academic research sector. This is for the reason that the types of communication in VANETs, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I), become highly suitable for intelligent transportation system (ITS)-oriented applications. Due to the individuality of VANETs, such as random and unpredictable mobility, high speed, stability, efficiency, and effectiveness of VANETs are reduced during the process of communication. An effective routing model is needed to overcome this problem. In this paper, mobility prediction in an advanced destination routing model (MACH-DAR) is proposed to improve the efficiency of the VANETs. This MACH-DAR approach is divided into three segments: Advanced CH (ACH) selection, ACH in destination-aware routing model (ACH-DAR), and mobility prediction in ACH-DAR. The proposed MACH-DAR is mainly used to improve the efficiency of VANETs through clustering, destination-aware transmission of inter-cluster and intra-cluster methods, and mobility prediction to avoid congestion. The parameters that are used for result calculation are energy efficiency, packet delivery ratio, routing overhead, and throughput. The earlier research used for the comparative analysis was CP-DCM and CR-DPBP. The evaluation of the results shows that the proposed MACH-DAR approach produced higher energy efficiency, packet delivery ratio, and throughput and lower routing overhead than the earlier works.