Task urgency-based resource allocation algorithm in industrial Internet of things

ZOU Hong; ZHUO Sai; ZHANG Hong; ZHANG Mingxing; WU Dapeng

doi:10.11959/j.issn.1000-0801.2024018

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Task urgency-based resource allocation algorithm in industrial Internet of things

Research and Development | 更新时间：2024-06-05

- Task urgency-based resource allocation algorithm in industrial Internet of things
- Telecommunications Science Vol. 40, Issue 3, Pages: 29-38(2024)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(62271096;U20A20157);Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202000626);University Innovation Research Group of Chongqing(CXQT20017);Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04);Chongqing Postdoctoral Science Special Foundation(2021XM3058);Chongqing Natural Science Foundation of China(CSTB2023NSCQ-LZX0134)
- DOI：10.11959/j.issn.1000-0801.2024018
  CLC： TN929
- Received：01 September 2023，
  
  Revised：20 November 2023，
  
  Published：20 March 2024
- 稿件说明：
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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.

关键词

Keywords

references

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