Dynamic QoS-aware intelligent edge computing resource management algorithm for body area networks

MU Siqi; WEN Shuo; LU Yang; AI Bo

doi:10.11959/j.issn.2096-3750.2024.00443

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Dynamic QoS-aware intelligent edge computing resource management algorithm for body area networks

Topic: Computing Power Internet of Things | 更新时间：2025-01-20

- Dynamic QoS-aware intelligent edge computing resource management algorithm for body area networks
- Chinese Journal on Internet of Things Vol. 8, Issue 4, Pages: 45-53(2024)
- 作者机构：
  
  1.北京体育大学体育工程学院，北京 100084
  2.北京交通大学计算机与信息技术学院，北京 100044
  3.北京交通大学电子信息工程学院，北京 100044
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62101025)
- DOI：10.11959/j.issn.2096-3750.2024.00443
  CLC：
- Received：21 October 2024，
  
  Revised：2024-11-19，
  
  Published：10 December 2024
- 稿件说明：
移动端阅览
穆司琪,文硕,陆杨等.面向动态QoS感知的体域网智能边缘算力资源管理算法[J].物联网学报,2024,08(04):45-53.

MU Siqi,WEN Shuo,LU Yang,et al.Dynamic QoS-aware intelligent edge computing resource management algorithm for body area networks[J].Chinese Journal on Internet of Things,2024,08(04):45-53.
穆司琪,文硕,陆杨等.面向动态QoS感知的体域网智能边缘算力资源管理算法[J].物联网学报,2024,08(04):45-53. DOI： 10.11959/j.issn.2096-3750.2024.00443.

MU Siqi,WEN Shuo,LU Yang,et al.Dynamic QoS-aware intelligent edge computing resource management algorithm for body area networks[J].Chinese Journal on Internet of Things,2024,08(04):45-53. DOI： 10.11959/j.issn.2096-3750.2024.00443.

摘要

体域网（BAN

body area network）是医疗物联网在个人健康监测领域的关键技术，融合边缘计算实现生理数据实时监测、紧急预警和治疗诊断智能化等服务。然而，体域网中感知节点计算任务的服务质量（QoS

quality of service）随感知数据的紧急程度动态变化，现有的边缘算力网络资源分配方法难以高效灵活地保障体域网中多源异质任务的动态QoS。对长时程动态QoS感知的计算卸载和边缘算力随机优化问题进行了研究。考虑各体域网多源任务优先级和信道状态变化的马尔可夫性质，首先将原始的随机优化问题转化为无穷视域的马尔可夫决策过程问题。然后，构建各体域网的多源任务优先级序列，提出融合近端策略优化（PPO

proximal policy optimization）的深度强化学习任务卸载及算力分配在线决策算法。仿真结果表明，所提的决策算法优于现有基准算法，可有效地满足体域网中任务动态优先级需求，并降低任务完成所需的能量消耗和平均时延。

Abstract

Body area network (BAN) is a key technology of the medical Internet of things for personal health monitoring. Integrated with edge computing

it realizes real-time monitoring of physiological data

emergency warning

and intelligent treatment and diagnosis. However

the quality of service (QoS) requirements of the computing tasks in BAN varie with the urgency of the sensing data. The existing resource allocation methods in edge computing network are difficult to efficiently and flexibly support dynamic QoS of multi-source heterogeneous tasks in BAN. A dynamic QoS-aware stochastic optimization problem on computation offloading decisions and edge computing resource allocation was studied. Firstly

considering the Markov nature of multi-source task priorities and channel state changes in BAN

the original stochastic optimization problem was transformed into an infinite horizon Markov decision process problem. Then

a multi-source task priority sequence for each BAN was constructed and an online decision-making method that integrated proximal policy optimization (PPO) was proposed for task offloading and computing resource allocation. The simulation results show that the proposed optimization scheme outperforms existing baseline methods

effectively meeting the dynamic priority requirements of tasks in BAN and reducing the energy consumption as well as the average delay required for task completion.

关键词

Keywords

references

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