计算卸载
计算机科学
计算
分布式计算
车载自组网
边缘计算
任务(项目管理)
回程(电信)
移动边缘计算
GSM演进的增强数据速率
计算机网络
无线自组网
服务器
无线
基站
工程类
算法
人工智能
系统工程
电信
作者
Lei Liu,Ming Zhao,Ming Yu,Mian Ahmad Jan,Dapeng Lan,Amir Taherkordi
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:: 1-14
被引量:64
标识
DOI:10.1109/tits.2022.3142566
摘要
Vehicular Edge Computing (VEC) has gained increasing interest due to its potential to provide low latency and reduce the load in backhaul networks. In order to meet drastically increasing computation demands from emerging ever-growing vehicular applications, e.g., autonomous driving, abundant computation resources of individual vehicles can play a crucial role in task execution in a VEC scenario, that can further contribute in considerably improving user experience. This is however an extremely challenging task due to high mobility of vehicles that can easily lead to intermittent connectivity, thereby disrupting on-going task processing. In this paper, we propose a task offloading scheme by exploiting multi-hop vehicle computation resources in VEC based on mobility analysis of vehicles. In addition to the vehicles within one hop from the task vehicle that generates computation tasks, certain multi-hop vehicles that meet the given requirements in terms of link connectivity and computation capacity, are also leveraged to carry out the tasks offloaded by the task vehicle. An optimization problem is formulated for the task vehicle to minimize the weighted sum of execution time and computation cost of all tasks. A semidefinite relaxation approach with an adaptive adjustment procedure is proposed to solve the formulated optimization problem for obtaining the corresponding offloading decisions. The simulation results show that our proposed offloading scheme can achieve significant improvement in terms of response delay by at least 34% compared with the other algorithms (e.g., local processing and random offloading).
科研通智能强力驱动
Strongly Powered by AbleSci AI