vConnect: V2V Connectivity Prediction and Independent Task Offloading Framework in Vehicular Edge Computing
Vehicular Edge Computing enables the utilization of idle resources on vehicles. Task offloading schemes have been widely investigated to satisfy the need for high computational power. In this paper, we propose a novel V2V connectivity prediction and independent task offloading framework for vehicula...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11072159/ |
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| Summary: | Vehicular Edge Computing enables the utilization of idle resources on vehicles. Task offloading schemes have been widely investigated to satisfy the need for high computational power. In this paper, we propose a novel V2V connectivity prediction and independent task offloading framework for vehicular edge computing, called vConnect. There are three necessary modules for supporting the overall task offloading process: the mobility prediction module, deadline-constraint task partitioning module, and independent task offloading module. This framework aims to maximize the success rate of task offloading within the deadline in urban scenarios. For practicality in the various tasks, the deadline-constraint task partitioning was introduced. Owing to the challenging characteristics of vehicular networks, we introduced a new metric, the so-called V2V connectivity time, which can efficiently represent the duration of uninterrupted communication between vehicles. We introduced the independent task offloading module to select and offload subtasks to multiple optimal candidate vehicles in a one-to-one relationship. We evaluated the performance of our vConnect in both the realistic movement of vehicles in an urban scenario and the real trace file covering the Central Business District (CBD) in Bangkok, Thailand, using OMNeT++, Veins, SUMO, and Python programming. The results show that our vConnect outperforms existing baselines in terms of the success ratio, especially when the required offloading time exceeds 10 s in urban scenarios and 30 s in real-world scenarios. Specifically, our vConnect achieves a 100% success rate in most urban scenarios and up to 94% in the real-world cases, demonstrating its effectiveness in improving task offloading performance. |
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| ISSN: | 2169-3536 |