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

As vehicular applications become increasingly complex, their computational demands often exceed the capabilities of individual vehicles. Vehicular Edge Computing (VEC) alleviates this limitation by enabling task delegation to nearby edge resources; however, high mobility, dynamic topology, and fluctuating vehicle density make real-time offloading decisions challenging. To address these issues, we propose a performance-optimized Vehicle-to-Vehicle (V2V) task offloading framework for dense and dynamic Vehicular Ad-hoc Networks (VANETs). The framework follows a two-stage design: (i) context-aware edge-node selection based on live topology capture via periodic beaconing, and (ii) cumulative score-based dynamic priority queuing at the selected edge node. The priority score jointly considers relative speed, distance, task size, and task priority, and its weights can be tuned to match application requirements and network conditions. Using OMNeT++/Veins/SUMO simulations, we evaluate dissemination and system delay, packet delivery ratio, task completion/success rate, and task processing failure rate. Results show improvements of up to 27% in system delay, 18% in packet delivery ratio, and 24% in task completion ratio compared with representative baselines, demonstrating robust performance under high density and mobility.