面向动态QoS感知的体域网智能边缘算力资源管理算法

穆司琪; 文硕; 陆杨; 艾渤

doi:10.11959/j.issn.2096-3750.2024.00443

您当前的位置：

首页 >

文章列表页 >

面向动态QoS感知的体域网智能边缘算力资源管理算法

专题：算力物联网 | 更新时间：2025-01-20

- 面向动态QoS感知的体域网智能边缘算力资源管理算法
- Dynamic QoS-aware intelligent edge computing resource management algorithm for body area networks
- 物联网学报 2024年8卷第4期页码：45-53
- 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：
  
  [ "穆司琪（1993‒ ），女，博士，北京体育大学体育工程学院助理教授，主要研究方向为医疗物联网、边缘计算、体域网及其在健康医疗领域的应用等。" ]
  [ "文硕（2000‒ ），男，北京体育大学体育工程学院硕士生，主要研究方向为体域网及其在健康医疗领域的应用等。" ]
  [ "陆杨（1992‒ ），男，博士，北京交通大学计算机与信息技术学院教授，主要研究方向为移动通信。" ]
  [ "艾渤（1974‒ ），男，博士，北京交通大学电子信息工程学院教授，主要研究方向为移动通信。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62101025)
- DOI：10.11959/j.issn.2096-3750.2024.00443
  中图分类号：
- 纸质出版日期：2024-12-10，
  
  收稿日期：2024-10-21，
  
  修回日期：2024-11-19，
- 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, 2024,8(4):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, 8(4): 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, 2024,8(4):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, 8(4): 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

医疗物联网边缘计算资源管理服务质量

Keywords

medical Internet of thingsedge computingresource managementQoS

references

PHILIP N Y, RODRIGUES J J P C, WANG H G, et al. Internet of things for in-home health monitoring systems: current advances, challenges and future directions[J]. IEEE Journal on Selected Areas in Communications, 2021, 39(2): 300-310.

梁峻阁, 宋怡然, 孙杨帆, 等. 基于可穿戴与可植入技术的人体健康物联网研究进展[J]. 物联网学报, 2023, 7(2): 26-34.

LIANG J G, SONG Y R, SUN Y F, et al. Research progress of human health IoT based on wearable and implantable techniques[J]. Chinese Journal on Internet of Things, 2023, 7(2): 26-34.

CORNET B, FANG H, NGO H, et al. An overview of wireless body area networks for mobile health applications[J]. IEEE Network, 2022, 36(1): 76-82.

寇家华, 唐雷, 乔峙, 等. 基于可穿戴计算的体域网技术应用现状与趋势研究[J]. 信息通信技术与政策, 2020(8): 75-79.

KOU J H, TANG L, QIAO Z, et al. Current situation and trend of body area network based on wearable computing[J]. Information and Communications Technology and Policy, 2020(8): 75-79.

QUY V K, HAU N V, ANH D V, et al. Smart healthcare IoT applications based on fog computing: architecture, applications and challenges[J]. Complex & Intelligent Systems, 2022, 8(5): 3805-3815.

MU S Q, LIAO S W, TAO K, et al. Intelligent fatigue detection based on hierarchical multi-scale ECG representations and HRV measures[J]. Biomedical Signal Processing and Control, 2024, 92: 106127.

HAYYOLALAM V, ALOQAILY M, ÖZKASAP Ö, et al. Edge-assisted solutions for IoT-based connected healthcare systems: a literature review[J]. IEEE Internet of Things Journal, 2022, 9(12): 9419-9443.

DUAN S J, WANG D, REN J, et al. Distributed artificial intelligence empowered by end-edge-cloud computing: a survey[J]. IEEE Communications Surveys & Tutorials, 2023, 25(1): 591-624.

张依琳, 梁玉珠, 尹沐君, 等. 移动边缘计算中计算卸载方案研究综述[J]. 计算机学报, 2021, 44(12): 2406-2430.

ZHANG Y L, LIANG Y Z, YIN M J, et al. Survey on the methods of computation offloading in mobile edge computing[J]. Chinese Journal of Computers, 2021, 44(12): 2406-2430.

DUAN S J, LYU F, WU H Q, et al. MOTO: mobility-aware online task offloading with adaptive load balancing in small-cell MEC[J]. IEEE Transactions on Mobile Computing, 2024, 23(1): 645-659.

XIAO Y L, XIAO L, WAN K P, et al. Reinforcement learning based energy-efficient collaborative inference for mobile edge computing[J]. IEEE Transactions on Communications, 2023, 71(2): 864-876.

ZHAO M X, YU J J, LI W T, et al. Energy-aware task offloading and resource allocation for time-sensitive services in mobile edge computing systems[J]. IEEE Transactions on Vehicular Technology, 2021, 70(10): 10925-10940.

ZHANG L J, SONG Q Y, WU M R, et al. Joint terminal pairing and multi-dimensional resource allocation for cooperative computation in a WP-MEC system[J]. IEEE Transactions on Green Communications and Networking, 2023, 7(3): 1447-1456.

EOM S, LEE H, PARK J, et al. Asynchronous protocol designs for energy efficient mobile edge computing systems[J]. IEEE Transactions on Vehicular Technology, 2021, 70(1): 1013-1018.

NING Z L, DONG P R, WANG X J, et al. Mobile edge computing enabled 5G health monitoring for Internet of medical things: a decentralized game theoretic approach[J]. IEEE Journal on Selected Areas in Communications, 2021, 39(2): 463-478.

ZHANG R R, LI H, QIAO Y Y, et al. Deep learning-based task offloading and time allocation for edge computing WBANs[C]//Proceedings of the GLOBECOM 2022—2022 IEEE Global Communications Conference. Piscataway: IEEE Press, 2022: 2206-2211.

YUAN X M, ZHANG Z D, FENG C J, et al. A DQN-based frame aggregation and task offloading approach for edge-enabled IoMT[J]. IEEE Transactions on Network Science and Engineering, 2023, 10(3): 1339-1351.

ZHANG L, YUAN X M, LUO J Q, et al. An adaptive resource allocation approach based on user demand forecasting for E-healthcare systems[C]//Proceedings of the 2022 IEEE International Conference on Communications Workshops (ICC Workshops). Piscataway: IEEE Press, 2022: 349-354.

YUAN X M, ZHU Y S, ZHAO Z Y, et al. An A3C-based joint optimization offloading and migration algorithm for SD-WBANs[C]//Proceedings of the 2020 IEEE Globecom Workshops (GC Wkshps. Piscataway: IEEE Press, 2020: 1-6.

SUTTON R S. Reinforcement learning: an introduction[J]. IEEE Transactions on Neural Networks, 2005, 16: 285-286.

SCHULMAN J, WOLSKI F, DHARIWAL P, et al. Proximal policy optimization algorithms[J]. arXiv perprint, 2017, arXiv: 1707.06347.

KWAK K S, ULLAH S, ULLAH N. An overview of IEEE 802.15.6 standard[C]//Proceedings of the 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2010). Piscataway: IEEE Press, 2010: 1-6.

CHANDRAKASAN A, SHENG S, BRODERSEN R. Low-power CMOS digital design[J]. IEEE Journal of Solid-State Circuits, 1992, 27: 473-484.

WANG H S, MOAYERI N. Finite-state Markov channel-a useful model for radio communication channels[J]. IEEE Transactions on Vehicular Technology, 1995, 44(1): 163-171.

CHEN X F, ZHANG H G, WU C, et al. Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning[J]. IEEE Internet of Things Journal, 2019, 6(3): 4005-4018.

LI H, XIONG K, LU Y, et al. Distributed design of wireless powered fog computing networks with binary computation offloading[J]. IEEE Transactions on Mobile Computing, 2023, 22(4): 2084-2099.

MU S Q, ZHONG Z D, ZHAO D M. Energy-efficient and delay-fair mobile computation offloading[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 15746-15759.

ZHANG R C, XIONG K, LU Y, et al. Energy efficiency maximization in RIS-assisted SWIPT networks with RSMA: a PPO-based approach[J]. IEEE Journal on Selected Areas in Communications, 2023, 41(5): 1413-1430.

ZHANG Y C, LU Y, ZHANG R C, et al. Deep reinforcement learning for secrecy energy efficiency maximization in RIS-assisted networks[J]. IEEE Transactions on Vehicular Technology, 2023, 72(9): 12413-12418.

JIANG F, QIN J W, LIU L, et al. Associative tasks computing offloading scheme in Internet of medical things with deep reinforcement learning[J]. China Communications, 2024, 21(4): 38-52.

YUAN X M, TIAN H S, ZHANG Z D, et al. A MEC offloading strategy based on improved DQN and simulated annealing for Internet of behavior[J]. ACM Transactions on Sensor Networks, 2023, 19(2): 1-20.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

面向情感计算的智慧教室系统构建以及优化

基于能量收集技术的协作卸载计算方案

车联网边缘计算场景下基于改进型NSGA-Ⅱ算法的边缘服务器部署决策

基于STM32的农业物联网病虫害图像识别算法研究

一种应用于边缘计算的区块链分片方案