基于能量收集技术的协作卸载计算方案

王珺; 赵浩东

doi:10.11959/j.issn.2096-3750.2024.00361

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基于能量收集技术的协作卸载计算方案

理论与技术 | 更新时间：2024-08-16

- 基于能量收集技术的协作卸载计算方案
- Collaborative offloading computing scheme based on energy harvesting technology
- 物联网学报 2024年8卷第2期页码：91-102
- 作者机构：
  
  南京邮电大学通信与信息工程学院，江苏南京 210003
- 作者简介：
  
  [ "王珺（1975‒ ），女，博士，南京邮电大学副教授，主要研究方向为物联网、边缘计算、下一代网络等。" ]
  [ "赵浩东（1998‒ ），男，南京邮电大学硕士生，主要研究方向为物联网和边缘计算。" ]
- 基金信息：
  
  江苏省研究生科研与实践创新计划;The Postgraduate Research and Practice Innovation Program of Jiangsu Province(46006CX21732);江苏省重点研发计划;The Key Research and Development Program of Jiangsu Province(BE2020084-5)
- DOI：10.11959/j.issn.2096-3750.2024.00361
  中图分类号：
- 纸质出版日期：2024-06-10，
  
  收稿日期：2023-03-27，
  
  修回日期：2024-06-15，
- 稿件说明：
移动端阅览
王珺,赵浩东.基于能量收集技术的协作卸载计算方案[J].物联网学报,2024,08(02):91-102.

WANG Jun,ZHAO Haodong.Collaborative offloading computing scheme based on energy harvesting technology[J].Chinese Journal on Internet of Things,2024,08(02):91-102.
王珺,赵浩东.基于能量收集技术的协作卸载计算方案[J].物联网学报,2024,08(02):91-102. DOI： 10.11959/j.issn.2096-3750.2024.00361.

WANG Jun,ZHAO Haodong.Collaborative offloading computing scheme based on energy harvesting technology[J].Chinese Journal on Internet of Things,2024,08(02):91-102. DOI： 10.11959/j.issn.2096-3750.2024.00361.

摘要

近年来，物联网（IoT）应用对设备可使用的能量要求不断提高，能量收集（EH）技术成为缓解边缘计算中设备能量短缺问题并延长电池寿命的重要途径。然而，当环境中可再生能源不充足时，设备电量耗尽会导致任务中断，影响物联网性能。为了解决这一问题，提出了一种联合能量收集和设备间（D2D）通信技术的任务卸载框架，采用基于深度强化学习（DRL）的边缘协作卸载计算方案，自主进行决策并使用模拟退火算法解决资源分配问题，以最小化系统运行总成本。对稳定和极端两种能量环境进行仿真，结果表明，该方案在单用户多设备场景下可稳定运行且具有成本效益。

Abstract

In recent years

the energy requirements for devices in internet of things (IoT) applications have increased

making energy harvesting (EH) technology an important way to alleviate the energy shortage problem in edge computing and extend the battery life of devices. However

when there was insufficient renewable energy in the environment

the depletion of device power can cause task interruption and affect the performance of IoT. To solve this problem

a task offloading framework that combined energy harvesting and device-to-device (D2D) communication technology was proposed

using a deep reinforcement learning (DRL)-based edge collaborative offloading computing scheme to make autonomous decisions and solve resource allocation problems using simulated annealing algorithms to minimize the total cost of system operation. Simulation results on stable and extreme energy environments show that the proposed scheme can run stably and cost-effectively in single-user multiple-device scenarios.

关键词

边缘计算能量收集设备间通信深度强化学习

Keywords

edge computingenergy harvestingdevice-to-device communicationdeep reinforcement learning

references

MAO Y Y, YOU C S, ZHANG J, et al. A survey on mobile edge computing: the communication perspective[J]. IEEE Communications Surveys & Tutorials, 2017, 19(4): 2322-2358.

GARCIA LOPEZ P, MONTRESOR A, EPEMA D, et al. Edge-centric computing[J]. ACM SIGCOMM Computer Communication Review, 2015, 45(5): 37-42.

王英恺, 王青山. 能量收集无线通信系统中基于强化学习的能量分配策略[J]. 计算机科学, 2021, 48(7): 333-339.

WANG Y K, WANG Q S. Reinforcement learning based energy allocation strategy for multi-access wireless communications with energy harvesting[J]. Computer Science, 2021, 48(7): 333-339.

MA D, LAN G, HASSAN M, et al. Sensing, computing, and communications for energy harvesting IoTs: A survey[J]. IEEE Commun. Surveys Tuts.,2020, 22(2): 1222-1250.

ZENG D Z, PAN S L, CHEN Z L, et al. An MDP-based wireless energy harvesting decision strategy for mobile device in edge computing[J]. IEEE Network, 2019, 33(6): 109-115.

PADHY A, JOSHI S, BITRAGUNTA S, et al. A survey of energy and spectrum harvesting technologies and protocols for next generation wireless networks[J]. IEEE Access, 2021(9): 1737-1769.

MEHRABI M, YOU D, LATZKO V, et al. Device-enhanced MEC: multi-access edge computing (MEC) aided by end device computation and caching: A survey[J]. IEEE Access, 2019(7): 166079-166108.

ZHOU H, WU T, ZHANG H J, et al. Incentive-driven deep reinforcement learning for content caching and D2D offloading[J]. IEEE Journal on Selected Areas in Communications, 2021, 39(8): 2445-2460.

马惠荣, 陈旭, 周知, 等. 绿色能源驱动的移动边缘计算动态任务卸载[J]. 计算机研究与发展, 2020, 57(9): 1823-1838.

MA H R, CHEN X, ZHOU Z, et al. Dynamic task offloading for mobile edge computing with green energy[J]. Journal of Computer Research and Development, 2020, 57(9): 1823-1838.

ZHANG L T, XIONG K, ZHANG Y. UAV-assisted wireless energy harvesting fog computing network optimization method[J].Journal of Software, 2019, 30(1): 9-17.

BAKAR A, HESTER J. Making sense of intermittent energy harvesting[C]//Proceedings of the 6th International Workshop on Energy Harvesting & Energy-Neutral Sensing Systems. New York: ACM Press, 2018: 32-37.

TIAN X Z, YAO C, ZHAO C, et al. 5G network-oriented mobile edge computation offloading strategy[J]. Computer Science, 2020, 47(11A): 286-290.

池凯凯, 徐欣晨, 魏欣晨. 射频能量捕获传感网中满足节点吞吐量需求的基站最少化部署方案[J]. 计算机科学, 2018, 45(S1): 332-336.

CHI K K, XU X C, WEI X C. Minimum deployment scheme of base stations to meet node throughput requirements in RF energy capture sensor networks[J]. Computer Science, 2018, 45(S1): 332-336.

YUAN F C, ZHANG Q T, JIN S, et al. Optimal harvest-use-store strategy for energy harvesting wireless systems[J]. IEEE Transactions on Wireless Communications, 2015, 14(2): 698-710.

ZHAO F J, CHEN Y, ZHANG Y C, et al. Dynamic offloading and resource scheduling for mobile-edge computing with energy harvesting devices[J]. IEEE Transactions on Network and Service Management, 2021, 18(2): 2154-2165.

MAO Y Y, ZHANG J, LETAIEF K B. Dynamic computation offloading for mobile-edge computing with energy harvesting devices[J]. IEEE Journal on Selected Areas in Communications, 2016, 34(12): 3590-3605.

WANG L H, SHAO H, LI J J, et al. Optimal multi-user computation offloading strategy for wireless powered sensor networks[J]. IEEE Access, 2020(8): 35150-35160.

SUN Y F, WU J G, CHEN L, et al. Latency optimization for mobile edge computing with dynamic energy harvesting[C]//Proceedings of 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). Piscataway: IEEE Press, 2019: 79-83.

TAN M X, CHEN B, PANG R M, et al. MnasNet: platform-aware neural architecture search for mobile[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2019: 2815-2823.

TAYLOR B, MARCO V S, WOLFF W, et al. Adaptive deep learning model selection on embedded systems[J]. ACM SIGPLAN Notices, 2018, 53(6): 31-43.

LI J, GAO H, LV T J, et al. Deep reinforcement learning based computation offloading and resource allocation for MEC[C]//Proceedings of 2018 IEEE Wireless Communications and Networking Conference (WCNC). Piscataway: IEEE Press, 2018: 1-6.

MIN M H, XIAO L, CHEN Y, et al. Learning-based computation offloading for IoT devices with energy harvesting[J]. IEEE Transactions on Vehicular Technology, 2019, 68(2): 1930-1941.

WANG Q, LENG S P, FU H R, et al. An IEEE 802.11p-based multichannel MAC scheme with channel coordination for vehicular ad hoc networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(2): 449-458.

XU J, CHEN L X, REN S L. Online learning for offloading and autoscaling in energy harvesting mobile edge computing[J]. IEEE Transactions on Cognitive Communications and Networking, 2017, 3(3): 361-373.

DU J B, ZHAO L Q, FENG J, et al. Computation offloading and resource allocation in mixed fog/cloud computing systems with Min-max fairness guarantee[J]. IEEE Transactions on Communications, 2018, 66(4): 1594-1608.

李茹杨, 彭慧民, 李仁刚, 等. 强化学习算法与应用综述[J]. 计算机系统应用, 2020, 29(12): 13-25.

LI R Y, PENG H M, LI R G, et al. Overview on algorithms and applications for reinforcement learning[J]. Computer Systems & Applications, 2020, 29(12): 13-25.

CHEN N, ZHANG S, QIAN Z Z, et al. When learning joins edge: real-time proportional computation offloading via deep reinforcement learning[C]//Proceedings of 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS). Piscataway: IEEE Press, 2019: 414-421.

HUANG L, BI S Z, ZHANG Y J A. Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks[J]. IEEE Transactions on Mobile Computing, 2020, 19(11): 2581-2593.

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.

刘朝阳, 穆朝絮, 孙长银. 深度强化学习算法与应用研究现状综述[J]. 智能科学与技术学报, 2020, 2(4): 314-326.

LIU Z Y, MU C X, SUN C Y. An overview on algorithms and applications of deep reinforcement learning[J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(4): 314-326.

庞峰. 模拟退火算法的原理及算法在优化问题上的应用[D]. 长春: 吉林大学, 2006.

PANG F. The principle of SA algorithm and algorithm's application on optimization problem[D]. Changchun: Jilin University, 2006.

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