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工业物联网中基于任务紧急程度的资源分配算法
Task urgency-based resource allocation algorithm in industrial Internet of things
- 电信科学 2024年40卷第3期 页码：29-38
- 作者机构：
1.重庆邮电大学通信与信息工程学院，重庆 400065
2.先进网络与智能互联技术重庆市高校重点实验室，重庆 400065
3.泛在感知与互联重庆市重点实验室，重庆 400065
- 作者简介：
[ "邹虹（1970- ），女，重庆邮电大学通信与信息工程学院副教授，主要研究方向为工业物联网、光无线融合网络等。" ]
[ "卓赛（1999- ），男，重庆邮电大学通信与信息工程学院硕士生，主要研究方向为工业物联网。" ]
[ "张鸿（1987- ），男，博士，重庆邮电大学通信与信息工程学院讲师，主要研究方向为工业物联网、光无线融合网络等。" ]
[ "张明兴（1999- ），男，重庆邮电大学通信与信息工程学院硕士生，主要研究方向为工业物联网。" ]
[ "吴大鹏（1979- ），男，博士，重庆邮电大学通信与信息工程学院教授，主要研究方向为工业物联网、泛在无线网络等。" ]
- 基金信息：国家自然科学基金资助项目(62271096;U20A20157);重庆市教委科学技术研究项目(KJQN202000626);重庆市高校创新研究群体项目(CXQT20017);重邮信通青创团队支持计划项目(SCIE-QN-2022-04);重庆市博士后研究项目特别资助项目(2021XM3058);重庆市自然科学基金创新发展联合基金资助项目(CSTB2023NSCQ-LZX0134)
DOI：10.11959/j.issn.1000-0801.2024018
中图分类号： TN929收稿日期：2023-09-01，
修回日期：2023-11-20，
纸质出版日期：2024-03-20
- 稿件说明：
移动端阅览
邹虹,卓赛,张鸿等.工业物联网中基于任务紧急程度的资源分配算法[J].电信科学,2024,40(03):29-38.
ZOU Hong,ZHUO Sai,ZHANG Hong,et al.Task urgency-based resource allocation algorithm in industrial Internet of things[J].Telecommunications Science,2024,40(03):29-38.
邹虹,卓赛,张鸿等.工业物联网中基于任务紧急程度的资源分配算法[J].电信科学,2024,40(03):29-38. DOI： 10.11959/j.issn.1000-0801.2024018.
ZOU Hong,ZHUO Sai,ZHANG Hong,et al.Task urgency-based resource allocation algorithm in industrial Internet of things[J].Telecommunications Science,2024,40(03):29-38. DOI： 10.11959/j.issn.1000-0801.2024018.
摘要
工业物联网中任务的生成通常具有连续性和周期性，并且任务对时延要求很高，这给系统成本带来了挑战。为应对这一挑战，提出了一种基于任务紧急程度的成本最小化资源分配算法。通过遗传算法优化任务的卸载策略和系统的资源分配策略，对于卸载的任务，根据任务的紧急程度进行调度，并在满足时延要求的前提下计算任务的最优发射功率。仿真结果表明，所提算法有效改善了系统总能耗成本。
Abstract
In the industrial Internet of things
the generation of tasks is observed to exhibit both continuity and periodicity
along with stringent latency requirements. These characteristics posed challenges to system's cost-efficiency. To address these challenges
a cost minimization resource allocation algorithm based on the urgency of tasks was proposed. By employing a genetic algorithm
the task offloading strategy and the system's resource allocation strategy were optimized. For offloaded tasks
they were scheduled according to their level of urgency. Additionally
the optimal transmission power for each task was calculated to meet latency constraints. Simulation results indicate that the proposed algorithm effectively reduces the overall energy cost of the system.
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Keywords
references
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