A Multihop Task Offloading Decision Model in MEC-Enabled Internet of Vehicles
A Multihop Task Offloading Decision Model in MEC-Enabled Internet of Vehicles
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DOI：
10.1109/jiot.2022.3143529
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发表时间：
2023-02-15
影响因子：
10.6
通讯作者：
Wan, Shaohua
中科院分区：
文献类型：
--
作者：
Chen, Chen;Zeng, Yini;Wan, Shaohua
移动边缘计算（MEC）作为一种新型网络技术，与车联网（IoV）相结合，可以有效提高任务计算和卸载的效率。然而，边缘计算的能力将严重局限于MEC服务器覆盖率较低的地区。此外，道路上存在大量暂时闲置计算资源的外围车辆，因此如何将这些车辆的资源投入使用成为需要考虑的首要问题。本文提出了一种基于任务执行效率的分布式多跳任务卸载决策模型，该模型主要由两部分组成：1）候选车辆选择机制，用于筛选能够参与卸载的相邻车辆; 2）任务卸载决策算法，用于获得任务卸载解。考虑到不同的跳数和无线通信范围对通信范围对任务完成的影响，在一般情况下，我们引入跳数$k$，并选择$k$跳无线通信范围内的相邻车辆作为候选车辆。然后，卸载问题被建模为一个广义的分配模型的约束条件，分别用贪婪算法和离散蝙蝠算法求解。结果表明，与任务车随机选择邻近车辆卸载和所有任务在本地完成的卸载方案相比，在不同任务数、任务所需计算能力和任务规模环境下，贪婪算法和蝙蝠算法下完成所有任务的卸载方案在时延性能上具有优势.此外，本文还探讨了跳数$k$对从$k$ hop范围内的相邻车辆中选择候选车辆的结果的影响。结果表明，k$的增加也会使候选车辆的数量增加，从而使时延降低。在本文设定的参数下，贪婪算法卸载方案完成所有任务所需的时延是蝙蝠算法方案时延的一个下界。贪婪算法方案与任务全部在本地完成的方案相比减少延迟0.2-2.4 s，并且与随机卸载方案相比减少延迟0.16-2.3 s。
As a new network technology, mobile-edge computing (MEC) combined with the Internet of Vehicles (IoV) can effectively improve the efficiency of task computing and offloading. However, the power of edge computing will be severely limited to the areas with poor MEC server coverage. Furthermore, there are a number of peripheral vehicles with temporarily idle computing resources on the road, so how to put the resources of these vehicles into use becomes the primary issue to be considered. In this article, a distributed multihop task offloading decision model for task execution efficiency is developed, which mainly consists of two parts: 1) a candidate vehicle selection mechanism for screening the neighboring vehicles that can participate in offloading and 2) a task offloading decision algorithm for obtaining the task offloading solution. Considering the impact of different hop and wireless communication ranges on communication ranges on task completion in a generic scenario, we introduce the hop count $k$ and select the neighboring vehicles in the $k$ -hop wireless communication range as the candidate vehicles. Then, the problem of offloading is modeled as a generalized allocation model with constraints which is solved by the greedy algorithm and discrete bat algorithm, respectively. The results show that compared with the scheme in which the task vehicle randomly selects the neighboring vehicles to offload and the scheme that all tasks are completed locally, the offloading scheme in which all tasks are completed under the greedy algorithm or bat-based algorithm has advantages in time delay performance in terms of different task number, task required computation power, and task size environment. Besides, this article also explores the influence of hop count $k$ on the results when selecting candidate vehicles from the neighboring vehicles within the range of $k$ hop. The results show that the increase of $k$ will also increase the number of candidate vehicles, which makes the time delay lower. Under the parameters set in this article, the time delay required for the greedy algorithm offloading scheme to complete all tasks is a lower bound on the time delay of the bat algorithm scheme. The greedy algorithm scheme reduces latency by 0.2-2.4 s compared to the scheme where tasks are all completed locally, and it reduces latency by 0.16-2.3 s compared to the random offloading scheme.