移动Ad Hoc云中基于能量收集的分布式卸载方案研究

鲜永菊; 郭陈榕; 夏士超; 李云

doi:10.11999/JEIT211165

移动Ad Hoc云中基于能量收集的分布式卸载方案研究

doi: 10.11999/JEIT211165

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
重庆邮电大学软件工程学院重庆 400065

基金项目: 国家自然科学基金 (62071077)

详细信息

作者简介:
鲜永菊：女，博士，副教授，研究方向为无线移动通信、物联网和边缘计算

郭陈榕：女，硕士生，研究方向为移动Ad Hoc云计算、边缘计算

夏士超：男，博士，研究方向为物联网、边缘计算

李云：男，博士，教授，研究方向为无线移动通信、物联网和边缘计算

通讯作者:
李云　liyun@cqupt.com

中图分类号: TN92
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- 被引次数: 0
出版历程
- 收稿日期: 2021-10-25
- 修回日期: 2022-06-20
- 网络出版日期: 2022-06-25
- 刊出日期: 2023-01-17

Research on Distributed Offloading Scheme Based on Energy Harvesting in Mobile Ad Hoc Cloud

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62071077)

摘要

摘要: 在物联网快速发展的驱动下，移动Ad Hoc云计算和能量收集(EH)技术通过共享邻近设备的闲置资源满足数据处理等需求，实现绿色通信。然而，由于Ad Hoc云网络的时变性以及EH的随机不稳定性，合理的任务卸载方案研究面临着严峻的挑战。针对以上问题，该文运用Lyapunov优化理论和博弈论，提出一种分布式动态卸载方案。理性的终端设备不可能无偿地为其他终端设备服务，为了鼓励终端设备积极参与计算卸载过程，提出一种基于动态报价的激励机制。相比于现有方案，仿真结果表明所提方案可以有效提升系统收益，稳定电池能量和减少任务队列积压。
- 移动Ad Hoc云 /
- 能量收集 /
- 分布式卸载 /
- 激励机制 /
- 博弈论
Abstract: Driven by the rapid development of the Internet of Things, mobile Ad Hoc cloud computing and Energy Harvesting (EH) technologies meet the needs of data processing by sharing the idle resources of adjacent devices, and realize green communication. However, due to the time-varying nature of Ad Hoc cloud networks and the stochastic instability of EH, the research on reasonable task offloading schemes faces severe challenges. Considering the above problems, a distributed dynamic offloading scheme is proposed in this paper by using Lyapunov optimization theory and game theory. It is impossible for rational terminal equipment to serve other terminal equipment for free, in order to encourage terminal equipment to participate actively in the calculation offloading process, an incentive mechanism based on dynamic quotation is proposed in this paper. Compared with the existing schemes, the simulation results show that the proposed scheme can effectively improve system revenue, stabilize battery energy and reduce task queue backlog.
- Mobile Ad Hoc cloud /
- Energy Harvesting (EH) /
- Distributed offloading /
- Incentive mechanism /
- Game theory

HTML全文

图 1 Ad Hoc云协作系统模型图

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图 2 资源动态报价过程

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图 3 任务队列积压及电池能量

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图 4 不同方案对比

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参考文献(19)

[1]	XIANG Zhengzhe, DENG Shuiguang, TAHERI J, et al. Dynamical service deployment and replacement in resource-constrained edges[J]. Mobile Networks and Applications, 2020, 25(3): 674–689. doi: 10.1007/s11036-019-01449-7
[2]	FERRER A J, MARQUÈS J M, and JORBA J. Towards the Decentralised cloud: Survey on approaches and challenges for mobile, ad hoc, and edge computing[J]. ACM Computing Surveys, 2019, 51(6): 111. doi: 10.1145/3243929
[3]	YUN Sining, ZHANG Yongwei, XU Qi, et al. Recent advance in new-generation integrated devices for energy harvesting and storage[J]. Nano Energy, 2019, 60: 600–619. doi: 10.1016/j.nanoen.2019.03.074
[4]	WANG Xiaojie, NING Zhaolong, HU Xiping, et al. Future communications and energy management in the internet of vehicles: Toward intelligent energy-harvesting[J]. IEEE Wireless Communications, 2019, 26(6): 87–93. doi: 10.1109/MWC.001.1900009
[5]	ALAM T and RABABAH B. Convergence of MANET in communication among smart devices in IoT[J]. International Journal of Wireless and Microwave Technologies(IJWMT) , 2020, 9(2): 1–10. doi: 10.5815/ijwmt.2019.02.01
[6]	SHAH S C. An energy-efficient resource management system for a mobile ad hoc cloud[J]. IEEE Access, 2018, 6: 62898–62914. doi: 10.1109/ACCESS.2018.2876600
[7]	ZHANG Fenghui, DENG Ruilong, and LIANG Hao. An optimal real-time distributed algorithm for utility maximization of mobile ad hoc cloud[J]. IEEE Communications Letters, 2018, 22(4): 824–827. doi: 10.1109/LCOMM.2018.2804928
[8]	ZHOU Bowen, SRIRAMA S N, and BUYYA R. An auction-based incentive mechanism for heterogeneous mobile clouds[J]. Journal of Systems and Software, 2019, 152: 151–164. doi: 10.1016/j.jss.2019.03.003
[9]	TANG Qinqin, XIE Renchao, HUANG Tao, et al. Dynamic computation offloading with imperfect state information in energy harvesting small cell networks: A partially observable stochastic game[J]. IEEE Wireless Communications Letters, 2020, 9(8): 1300–1304. doi: 10.1109/LWC.2020.2989147
[10]	TANG Qinqin, XIE Renchao, YU F A, et al. Decentralized computation offloading in IoT fog computing system with energy harvesting: A DEC-POMDP approach[J]. IEEE Internet of Things Journal, 2020, 7(6): 4898–4911. doi: 10.1109/JIOT.2020.2971323
[11]	ZHANG Guanglin, ZHANG Wenqian, CAO Yu, et al. Energy-delay tradeoff for dynamic offloading in mobile-edge computing system with energy harvesting devices[J]. IEEE Transactions on Industrial Informatics, 2018, 14(10): 4642–4655. doi: 10.1109/TII.2018.2843365
[12]	XU Jie, CHEN Lixing, and REN Shaolei. 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. doi: 10.1109/TCCN.2017.2725277
[13]	ZHONG Weifeng, XIE Kan, LIU Yi, et al. Online control and near-optimal algorithm for distributed energy storage sharing in smart grid[J]. IEEE Transactions on Smart Grid, 2020, 11(3): 2552–2562. doi: 10.1109/TSG.2019.2957426
[14]	张龙, 曹傧. Ad-hoc移动云环境下采取随机规划和买卖博弈的任务卸载方法[J]. 电子与信息学报, 2018, 40(7): 1731–1737. doi: 10.11999/JEIT170895 ZHANG Long and CAO Bin. Stochastic programming and buyer-seller game methods for workload distribution in an ad-hoc mobile cloud[J]. Journal of Electronics &Information Technology, 2018, 40(7): 1731–1737. doi: 10.11999/JEIT170895
[15]	夏士超, 姚枝秀, 鲜永菊, 等. 移动边缘计算中分布式异构任务卸载算法[J]. 电子与信息学报, 2020, 42(12): 2891–2898. doi: 10.11999/JEIT190728 XIA Shichao, YAO Zhixiu, XIAN Yongju, et al. A distributed heterogeneous task offloading methodology for mobile edge computing[J]. Journal of Electronics &Information Technology, 2020, 42(12): 2891–2898. doi: 10.11999/JEIT190728
[16]	NEELY M J and HUANG Longbo. Dynamic product assembly and inventory control for maximum profit[C]. The 49th IEEE Conference on Decision and Control, Atlanta, USA, 2010: 2805–2812.
[17]	赵星, 彭建华, 游伟. 基于Lyapunov优化的隐私感知计算卸载方法[J]. 电子与信息学报, 2020, 42(3): 704–711. doi: 10.11999/JEIT190170 ZHAO Xing, PENG Jianhua, and YOU Wei. A privacy-aware computation offloading method based on Lyapunov optimization[J]. Journal of Electronics &Information Technology, 2020, 42(3): 704–711. doi: 10.11999/JEIT190170
[18]	XIA Shichao, YAO Zhixiu, LI Yun, et al. Online distributed offloading and computing resource management with energy harvesting for heterogeneous MEC-enabled IoT[J]. IEEE Transactions on Wireless Communications, 2021, 20(10): 6743–6757. doi: 10.1109/TWC.2021.3076201
[19]	ZHANG Yongqiang, HE Jianbo, and GUO Songtao. Energy-efficient dynamic task offloading for energy harvesting mobile cloud computing[C]. 2018 IEEE International Conference on Networking, Architecture and Storage, Chongqing, China, 2018: 1–4.