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A performance-optimized V2V task offloading framework for real-time vehicular communication.

Tariq Qayyum1, Asadullah Tariq1,2,3, Ikbal Taleb3

  • 1College of Information Technology, United Arab Emirates University, Al Ain, 17551, UAE.

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|March 24, 2026
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Summary
This summary is machine-generated.

This study introduces a Vehicle-to-Vehicle (V2V) task offloading framework for vehicular edge computing (VEC) in dynamic networks. The optimized V2V offloading improves system performance and task completion rates.

Keywords:
Edge computingIoTTask offloadingV2VVANETsVehicular communication

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Area of Science:

  • Computer Science
  • Networking
  • Intelligent Transportation Systems

Background:

  • Vehicular applications face computational demands exceeding individual vehicle capacity.
  • Vehicular Edge Computing (VEC) enables task offloading to edge resources.
  • High mobility and dynamic topology in Vehicular Ad-hoc Networks (VANETs) challenge real-time offloading decisions.

Purpose of the Study:

  • To propose a performance-optimized Vehicle-to-Vehicle (V2V) task offloading framework for dense and dynamic VANETs.
  • To address challenges in real-time task offloading decisions within VEC environments.

Main Methods:

  • A two-stage framework: context-aware edge-node selection using periodic beaconing and cumulative score-based dynamic priority queuing.
  • Priority score considers relative speed, distance, task size, and task priority.
  • Evaluated using OMNeT++/Veins/SUMO simulations.

Main Results:

  • Achieved up to 27% improvement in system delay.
  • Demonstrated an 18% increase in packet delivery ratio.
  • Showcased a 24% enhancement in task completion ratio compared to baselines.

Conclusions:

  • The proposed V2V task offloading framework exhibits robust performance in dense and dynamic VANETs.
  • The framework effectively optimizes task offloading decisions for improved vehicular application performance.