基于计算卸载的电力物联网能效优化研究

doi:10.11930/j.issn.1004-9649.202010054

中国电力 ›› 2021, Vol. 54 ›› Issue (5): 28-34,45.DOI: 10.11930/j.issn.1004-9649.202010054

• 国家“十三五”智能电网重大专项专栏：（七）电力信息通信新技术在能源互联网中的研究与应用专栏 • 上一篇下一篇

基于计算卸载的电力物联网能效优化研究

刘世栋^1,2, 卜宪德^1,2, 刘川^1,2, 田峰³

1. 全球能源互联网研究院有限公司信息通信研究所，江苏南京 210003;
2. 国家电网公司电力通信网络技术实验室，江苏南京 210003;
3. 南京邮电大学宽带无线通信与传感网技术教育部重点实验室，江苏南京 210003

收稿日期:2020-10-19 修回日期:2021-02-23 发布日期:2021-05-05
作者简介:刘世栋(1971-),男,博士,高级工程师,从事电力物联网、软件定义网络、算力网络等领域研究,E-mail:2420493952@qq.com
基金资助:
国家电网公司科技项目(低时延安全可靠智能物联代理装置的研发及应用，SGZJDK00DYJS2000030)

Energy Efficiency Optimization Based on Computing Offloading for Internet of Things in Power Systems

LIU Shidong^1,2, BU Xiande^1,2, YU Qiang^1,2, TIAN Feng³

1. State Grid Laboratory of Electric Power Communication Network, Global Energy Interconnection Research Institute, Nanjing 210003, China;
2. State Grid Laboratory of Electric Power Communication Network, Nanjing 210003, China;
3. Key Laboratory of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Received:2020-10-19 Revised:2021-02-23 Published:2021-05-05
Supported by:
This work is supported by the Science and Technology Projects of State Grid Corporation of China (Development and Application of Low-Delay Safe and Reliable Intelligent IoT Agent Device, No.SGZJDK00DYJS2000030)

摘要/Abstract

摘要： 基于异构网络架构，考虑基于三层计算的电力物联网移动边缘计算架构，构建了各层计算的能量消耗模型和延迟模型，实现智能移动设备的任务从本地计算卸载到小基站的边缘服务器和宏基站边缘云服务器进行计算。针对电力物联网计算卸载的能耗最小化问题，采用变量替换分解问题，并利用目标函数单调性进行理论推导，得到优化的参数解。仿真结果表明，通过与其他方案相比，所提优化方案可以在较小的延迟条件下，实现优化的节能效果。

关键词: 电力物联网, 异构网络, 移动边缘计算, 计算卸载, 能效优化

Abstract: Based on the heterogeneous network architecture, in this paper by taking into account the mobile edge computing (MEC) architecture of Internet of things in power systems using three-tier computing, the energy consumption model and delay model are established respectively for each tier. Then the related tasks are offloaded from local computation in smart mobile device (SMD) to the MEC servers of small base station (SBS) and macro base station (MBS). To minimize the energy consumption due to the computing offloading, through variable substitution and decomposition, then by taking advantage of theoretical derivation based on the objective function monotonicity, the optimal solution is finally obtained. Simulation results show that, in comparison with other methods, the proposed scheme can achieve the effects of energy saving with less time delay.

Key words: Internet of Things in power system, heterogeneous network, mobile edge computing, computing offloading, energy-efficient optimization

刘世栋, 卜宪德, 刘川, 田峰. 基于计算卸载的电力物联网能效优化研究[J]. 中国电力, 2021, 54(5): 28-34,45.

LIU Shidong, BU Xiande, YU Qiang, TIAN Feng. Energy Efficiency Optimization Based on Computing Offloading for Internet of Things in Power Systems[J]. Electric Power, 2021, 54(5): 28-34,45.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202010054

https://www.electricpower.com.cn/CN/Y2021/V54/I5/28

参考文献

[1] KUNDUR P, BALU N J, LAUBY M G. Power system stability and control[M]. New York: McGraw-hill, 1994.
[2] SUN Y Z, LI X G, LIU Y M, et al. Edge computing terminal equipment planning method for real-time online monitoring service of power grid[C]//2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). Chengdu, China. IEEE, 2019: 35-41.
[3] CHOUIKHI S, MERGHEM-BOULAHIA L, ESSEGHIR M. A fog computing architecture for energy demand scheduling in smart grid[C]//2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC). Tangier, Morocco. IEEE, 2019: 1815-1821.
[4] SHI W S, CAO J, ZHANG Q, et al. Edge computing: vision and challenges[J]. IEEE Internet of Things Journal, 2016, 3(5): 637-646.
[5] ABBAS N, ZHANG Y, TAHERKORDI A, et al. Mobile edge computing: a survey[J]. IEEE Internet of Things Journal, 2018, 5(1): 450-465.
[6] REN J, GUO Y D, ZHANG D Y, et al. Distributed and efficient object detection in edge computing: challenges and solutions[J]. IEEE Network, 2018, 32(6): 137-143.
[7] KIANI A, ANSARI N. Toward hierarchical mobile edge computing: an auction-based profit maximization approach[J]. IEEE Internet of Things Journal, 2017, 4(6): 2082-2091.
[8] EL HABER E, NGUYEN T M, ASSI C. Joint optimization of computational cost and devices energy for task offloading in multi-tier edge-clouds[J]. IEEE Transactions on Communications, 2019, 67(5): 3407-3421.
[9] ZHANG H Q, XIAO Y, BU S R, et al. Computing resource allocation in three-tier IoT fog networks: a joint optimization approach combining stackelberg game and matching[J]. IEEE Internet of Things Journal, 2017, 4(5): 1204-1215.
[10] 李邱苹, 赵军辉, 贡毅. 移动边缘计算中的计算卸载和资源管理方案[J]. 电信科学, 2019, 35(3): 36-46
LI Qiuping, ZHAO Junhui, GONG Yi. Computation offloading and resource management scheme in mobile edge computing[J]. Telecommunications Science, 2019, 35(3): 36-46
[11] 谢人超, 廉晓飞, 贾庆民, 等. 移动边缘计算卸载技术综述[J]. 通信学报, 2018, 39(11): 138-155
XIE Renchao, LIAN Xiaofei, JIA Qingmin, et al. Survey on computation offloading in mobile edge computing[J]. Journal on Communications, 2018, 39(11): 138-155
[12] CHEN X, JIAO L, LI W Z, et al. Efficient multi-user computation offloading for mobile-edge cloud computing[J]. IEEE/ACM Transactions on Networking, 2016, 24(5): 2795-2808.
[13] WANG Y T, SHENG M, WANG X J, et al. Mobile-edge computing: partial computation offloading using dynamic voltage scaling[J]. IEEE Transactions on Communications, 2016, 64(10): 4268-4282.
[14] YOU C S, HUANG K B, CHAE H, et al. Energy-efficient resource allocation for mobile-edge computation offloading[J]. IEEE Transactions on Wireless Communications, 2017, 16(3): 1397-1411.
[15] WANG F, XU J, WANG X, et al. Joint offloading and computing optimization in wireless powered mobile-edge computing systems[J]. IEEE Transactions on Wireless Communications, 2018, 17(3): 1784-1797.
[16] HU X Y, WONG K K, YANG K. Wireless powered cooperation-assisted mobile edge computing[J]. IEEE Transactions on Wireless Communications, 2018, 17(4): 2375-2388.
[17] YU Y H, ZHANG J, LETAIEF K B. Joint subcarrier and CPU time allocation for mobile edge computing[C]//2016 IEEE Global Communications Conference (GLOBECOM). Washington, DC, USA. IEEE, 2016: 1-6.
[18] ZHANG K, MAO Y M, LENG S P, et al. Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks[J]. IEEE Access, 2016, 4: 5896-5907.
[19] 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.
[20] WANG C M, YU F R, LIANG C C, et al. Joint computation offloading and interference management in wireless cellular networks with mobile edge computing[J]. IEEE Transactions on Vehicular Technology, 2017, 66(8): 7432-7445.
[21] DAI Y Y, XU D, MAHARJAN S, et al. Joint computation offloading and user association in multi-task mobile edge computing[J]. IEEE Transactions on Vehicular Technology, 2018, 67(12): 12313-12325.
[22] HAN Q N, YANG B, MIAO G W, et al. Backhaul-aware user association and resource allocation for energy-constrained HetNets[J]. IEEE Transactions on Vehicular Technology, 2017, 66(1): 580-593.
[23] LYU X C, TIAN H, SENGUL C, et al. Multiuser joint task offloading and resource optimization in proximate clouds[J]. IEEE Transactions on Vehicular Technology, 2017, 66(4): 3435-3447.
[24] PEI Y S, PENG Z Y, WANG Z L, et al. Energy-efficient mobile edge computing: three-tier computing under heterogeneous networks[J]. Wireless Communications and Mobile Computing, 2020, 2020: 1-17.
[25] LI M Y, RUI L L, QIU X S, et al. Design of a service caching and task offloading mechanism in smart grid edge network[C]//2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC). Tangier, Morocco. IEEE, 2019: 249-254.

基于计算卸载的电力物联网能效优化研究

Energy Efficiency Optimization Based on Computing Offloading for Internet of Things in Power Systems

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	吴赞红. 面向强干扰电力物联网的低功耗数据回传方法[J]. 中国电力, 2024, 57(11): 191-198.
[2]	吴钢, 周金辉, 李慧. 面向边缘增强分布式电力无线传感网的资源分配[J]. 中国电力, 2023, 56(8): 77-85,98.
[3]	翟峰, 冯云, 程凯, 蔡绍堂, 于丽莹, 杨挺. 基于信息熵的多源电力物联终端设备信任度评价方法[J]. 中国电力, 2022, 55(5): 158-165.
[4]	赵保华, 王志皓, 陈连栋, 任春卉, 余发江, 徐庆. 电力物联网可信树形批量认证机制[J]. 中国电力, 2022, 55(5): 149-157.
[5]	赵丙镇, 王栋, 钱雪, 李军. 基于区块链的电力物联网信任网关设计与实现[J]. 中国电力, 2021, 54(7): 192-197.
[6]	佘蕊, 张宁池, 王艳茹, 郭丹丹, 马文洁, 刘卉, 张洁. 面向电力物联网的5G通信认知无线电NOMA系统研究[J]. 中国电力, 2021, 54(5): 35-45.
[7]	熊轲, 张锐晨, 王蕊, 钟桂东, 张煜. 5G助力电力物联网：网络架构与关键技术[J]. 中国电力, 2021, 54(3): 99-108.
[8]	刘铭, 刘念, 韩晓艺, 彭林宁, 付华, 陈一悰. 一种基于射频指纹的电力物联网设备身份识别方法[J]. 中国电力, 2021, 54(3): 80-88.
[9]	林洁瑜, 崔维平. 基于双链区块链的电力数据资产交易系统架构[J]. 中国电力, 2021, 54(11): 164-170,180.
[10]	李苏秀, 刘林, 王雪, 代红才, 赵留军. 泛在电力物联网商业模式理论体系与设计架构[J]. 中国电力, 2019, 52(9): 1-9.
[11]	龙涛, 别朝红. 面向终端能源互联网的能效优化调度[J]. 中国电力, 2019, 52(6): 2-10.
[12]	王哲, 赵宏大, 朱铭霞, 周霞, 解相朋, 谢宏福, 臧必鹏. 电力无线专网在泛在电力物联网中的应用[J]. 中国电力, 2019, 52(12): 27-38.
[13]	陈家璘, 贺易, 李磊, 周正, 孙俊, 张洁. 泛在电力物联网传输网优化关键技术研究[J]. 中国电力, 2019, 52(12): 20-26,38.
[14]	王申华, 何湘威, 方小方, 陈冰松, 郭创新. 基于泛在电力物联网多源信息的电网动态风险评估系统[J]. 中国电力, 2019, 52(12): 10-19.
[15]	翟少磊，李博，张林山，唐标. 电力物联网信息化控制中一种高效的认知通信方法[J]. 中国电力, 2016, 49(8): 130-134.

模态框（Modal）标题

基于计算卸载的电力物联网能效优化研究

Energy Efficiency Optimization Based on Computing Offloading for Internet of Things in Power Systems

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics