OPTOS: A Strategy of Online Pre-Filtering Task Offloading System in Vehicular Ad Hoc Networks

IEEE Access

卷:10

Qiang Duan¹  Junjing He²  Yujie Wang²  Xin Du²  Zhihui Lu²  Jie Wu² 

[1] College of Information Sciences and Technology, The Pennsylvania State University, Abington, PA, USA;

[2] School of Computer Science, Fudan University, Shanghai, China;

关键词: Task offloading;    vehicular ad hoc network (VANET);    vehicular-to-vehicular (V2V);    vehicle mobility;   

DOI  :  10.1109/ACCESS.2022.3141456

来源: DOAJ

【 摘 要 】

The advanced services provided by vehicle ad hoc networks (VANETs) often require vehicles to process complex computing tasks that may not be completed by individual vehicles within a required delay limit. Offloading tasks to road side units (RSUs) is a typical approach to enhancing service performance in VANET; however, RSUs may not always have sufficient resources for handling all task offloading requests. With the increasing amount of computing capacities available on vehicles, offloading tasks to other vehicles offers a promising alternative to RSU-based task offloading. However, vehicle-to-vehicle task offloading in VANET faces some new challenges that have not been fully addressed, among which is the degraded delay performance caused by vehicle mobility. In order to solve this problem, we propose an Online Pre-filtering Task Offloading System (OPTOS) that is able to mitigate the impact of vehicle mobility on task offloading performance. OPTOS comprises a process that selects candidate vehicles for hosting offloaded tasks and an HGSA algorithm that assigns tasks to vehicles for minimizing task completion delay while balancing utilization of computing capacities on different vehicles. We have conducted extensive experiments using a real-world dataset for evaluating the performance of the proposed OPTOS. Obtained results indicate that OPTOS is effective for reducing task completion delay and increasing task success rate in various VANET scenarios with different levels of vehicle mobility.

【 授权许可】

Unknown