An Adaptive Data Rate-Based Task Offloading Scheme in Vehicular Networks

Chaofan CHEN, Wendi Nie, Yaoxin Duan, V. Lee, Kai Liu, Huamin Li
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Abstract

As an important application of Internet of Things (IoT), Internet of Vehicles (IoVs) can provide various valuable services which may require computation-intensive tasks under strict time constraints. Most traditional vehicles may not be able to process all these computation-intensive tasks locally because of the limitation of computing resources. Therefore, task offloading has been proposed, which allows vehicles to offload computation-intensive tasks to Mobile Edge Computing (MEC) servers. With the arising and development of intelligent vehicles, the concept of Vehicle as a Resource (VaaR) has been proposed as an important supplement to MEC, which enables intelligent vehicles to share computation resources with nearby vehicles. Most studies in VaaR generally assume that the transmission data rate of offloading tasks from one vehicle to another is fixed. However, in VaaR, due to the high mobility of vehicles, the communication distance between vehicles may change over time, resulting in changing data rate. Therefore, it is challenging to make offloading decisions (i.e., selecting proper vehicles as computation resource providers) while considering adaptive data rate. In this paper, we study task offloading in vehicular networks while considering adaptive data rate. We propose an Adaptive Data Rate-based Offloading algorithm named ADRO, which can not only achieve minimum energy consumption while satisfying time constraints, but also take adaptive data rate into consideration. Comprehensive experiments have been conducted to demonstrate the efficiency of the ADRO algorithm.
一种基于数据速率的自适应车载网络任务分流方案
作为物联网(IoT)的重要应用,车联网可以提供各种有价值的服务,这些服务可能需要在严格的时间限制下执行计算密集型任务。由于计算资源的限制,大多数传统车辆可能无法在本地处理所有这些计算密集型任务。因此,任务卸载被提出,它允许车辆将计算密集型任务卸载到移动边缘计算(MEC)服务器。随着智能汽车的兴起和发展,车辆即资源(Vehicle as a Resource, VaaR)的概念被提出,作为MEC的重要补充,使智能汽车能够与附近的车辆共享计算资源。大多数VaaR研究通常假设卸载任务从一辆车到另一辆车的传输数据速率是固定的。然而,在VaaR中,由于车辆的高移动性,车辆之间的通信距离可能会随着时间的推移而变化,从而导致数据速率的变化。因此,在考虑自适应数据速率的同时做出卸载决策(即选择合适的车辆作为计算资源提供者)是一项挑战。本文在考虑自适应数据速率的情况下,研究了车载网络中的任务卸载问题。提出了一种基于自适应数据速率的卸载算法ADRO,该算法既能在满足时间约束的情况下实现最小的能耗,又能考虑自适应数据速率。全面的实验验证了ADRO算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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