Joint resource allocation and task offloading for latency constraint and energy-sensing vehicular edge computing

GAO Wenxuan; YANG Xinjie; YANG Jiazhi; MA Nan

doi:10.11959/j.issn.1000-0801.2025049

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Joint resource allocation and task offloading for latency constraint and energy-sensing vehicular edge computing

Research and Development | 更新时间：2025-10-31

- Joint resource allocation and task offloading for latency constraint and energy-sensing vehicular edge computing
- Telecommunications Science Vol. 41, Issue 4, Pages: 143-163(2025)
- 作者机构：
  
  1.宁波大学信息科学与工程学院，浙江宁波 315211
  2.宁波艾欧迪互联科技有限公司，浙江宁波 315021
  3.北京邮电大学信息与通信工程学院，北京 100876
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2025049
  CLC： TP393
- Received：01 December 2024，
  
  Revised：2025-01-01，
  
  Published：20 April 2025
- 稿件说明：
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高文轩,杨新杰,杨家智等.时延约束与能量感知的车辆边缘计算协同资源分配与任务卸载[J].电信科学,2025,41(04):143-163.

GAO Wenxuan,YANG Xinjie,YANG Jiazhi,et al.Joint resource allocation and task offloading for latency constraint and energy-sensing vehicular edge computing[J].Telecommunications Science,2025,41(04):143-163.
高文轩,杨新杰,杨家智等.时延约束与能量感知的车辆边缘计算协同资源分配与任务卸载[J].电信科学,2025,41(04):143-163. DOI： 10.11959/j.issn.1000-0801.2025049.

GAO Wenxuan,YANG Xinjie,YANG Jiazhi,et al.Joint resource allocation and task offloading for latency constraint and energy-sensing vehicular edge computing[J].Telecommunications Science,2025,41(04):143-163. DOI： 10.11959/j.issn.1000-0801.2025049.

摘要

在车联网中，计算密集型和时延敏感型车载应用的出现对车辆的计算能力和处理速度提出了挑战。为此，车辆边缘计算（vehicular edge computing，VEC）应运而生，它通过利用周边车辆和路侧单元（roadside unit，RSU）的空闲计算资源来辅助车辆进行联合任务计算。然而，如何有效管理VEC系统的计算资源以实现系统性能优化，仍是一个亟待解决的问题。基于此，研究VEC系统中的协同资源分配和任务卸载问题，通过车辆到基础设施（vehicle-to-infrastructure，V2I）和车辆到车辆（vehicle-to-vehicle，V2V）的通信模式来实现部分任务的卸载，同时协同优化选择决策、任务卸载比例，以及车辆和RSU的计算资源分配，在保证任务完成的前提下最小化系统总能耗。由于该问题是一个NP难的混合整数非线性规划问题，将原问题解耦成多个子问题，并提出一种融合蚁群系统（ant colony system，ACS）、拉格朗日乘子和梯度法的优化方案。仿真结果表明，所提方案相较对比方案在降低任务未完成率和系统总能耗方面展现出更优越的性能。

Abstract

In the Internet of vehicles

the emergence of computationally intensive and latency-sensitive vehicular applications poses great challenges for computing time and power consumption. Vehicle edge computing (VEC) is therefore proposed to take advantages of the computing capabilities of vehicles and roadside unit (RSU). However

the efficient resource management for VEC systems to achieve optimised performance remains unsolved. Based on this

the issue of collaborative resource allocation and task offloading in the VEC system was studied

with partial tasks being offloaded through vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication modes. At the same time

the selection decisions

task offloading ratios

and the allocation of computing resources among vehicles and RSUs were collaboratively optimized

with the aim of minimizing the total system energy consumption while ensuring task completion. As the proble was a NP-hard mixed-integer nonlinear programming problem

the optimization problem was decoupled into multiple subproblems

and subsequently solved by using ant colony system (ACS)

Lagrange multipliers and gradient method. Extensive simulation results demonstrate that the proposed scheme significantly outperforms the benchmarking algorithms in terms of task incomplete rate and overall system energy consumption.

关键词

Keywords

references

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