[1] 陈行政,李聪波,李丽,等. 面向能效的多工步数控铣削工艺参数多目标优化模型[J]. 计算机集成制造系统, 2016,22(2):538-546.
[2] 毛水强,洪健,任华,等. 面向电力物联网的5G移动边缘计算任务卸载方法[J]. 电测与仪表, 2022,59(2):105-111.
[3] 朱思峰,孙恩林,柴争义. 移动边缘计算场景下基于免疫优化的任务卸载[J]. 西安电子科技大学学报, 2022,49(1):152-160.
[4] 贾觐,暴占彪. 改进GA的边缘计算任务卸载与资源分配策略[J]. 计算机工程与设计, 2021,42(11):3009-3017.
[5] 孙皓,杜俐洁,何荣希. TWDM-PON中基于网络编码的动态波长带宽分配算法[J]. 光通信技术, 2021,45(11):23-30.
[6] 冯平兴,郑美芳,李文翔. NG-PON2中的周期性动态带宽分配算法[J]. 光通信技术, 2022,46(5):11-14.
[7] LE T H T, TRAN N H, LEANH T, et al. Auction mechanism for dynamic bandwidth allocation in multi-tenant edge computing[J]. IEEE Transactions on Vehicular Technology, 2020,69(12):15162-15176.
[8] HE S X, WANG T Y, WANG S W, et al. Mobility-driven user-centric AP clustering in mobile edge computing-based ultra-dense networks[J]. Digital Communications and Networks, 2020,6(2):210-216.
[9] TORRES E, REALE R, SAMPAIO L, et al. A SDN/OpenFlow framework for dynamic resource allocation based on bandwidth allocation model[J]. IEEE Latin America Transactions, 2020,18(5):853-860.
[10] YAN Y D, YANG J, LIU C J, et al. On the actuator dynamics of dynamic control allocation for a small fixed-wing UAV with direct lift control[J]. IEEE Transactions on Control Systems Technology, 2020,28(3):984-991.
[11] RUFFINI M, AHMAD A, ZEB S, et al. Virtual DBA: Virtualizing passive optical networks to enable multi-service operation in true multi-tenant environments[J]. Journal of Optical Communications and Networking, 2020,12(4):B63
-B73.
[12] XUE X W, NAKAMURA F, PRIFTI K, et al. SDN enabled flexible optical data center network with dynamic bandwidth allocation based on photonic integrated wavelength selective switch[J]. Optics Express, 2020,28(6):8949-8958.
[13] HOSSEN M, HANAWA M. Delay and energy efficient dynamic bandwidth allocation algorithm for hybrid optical and wireless sensor networks[J]. Optical Fiber Technology, 2020, 55:102159-102159.
[14] HAMMADI A A. A framework for an energy-efficient bandwidth allocation approach through dynamic ONTs grouping in flexible GPON access networks[J]. International Journal of Communications, Network and System Sciences, 2020,13(1):1-14.
[15] MUSTAPHA O Z,HU Y F,SHERIFF R E, et al. Evaluation of bandwidth resource allocation using dynamic LSP and LDP in MPLS for wireless networks[J]. International Journal of Computing and Digital Systems, 2020,9(2):139-146.
[16] UZAWA H, HONDA K, NAKAMURA H, et al. Dynamic bandwidth allocation scheme for network-slicing-based TDM-PON toward the beyond-5G era[J]. Journal of Optical Communications and Networking, 2020,12(2):A135-A143.
[17] CHU X, LENG Z. Multiuser computing offload algorithm based on mobile edge computing in the Internet of Things environment[J]. Wireless Communications and Mobile Computing, 2022(1). DOI: 10.1155/2022/6107893.
[18] ZHU W J, XIANG T C, SHANG B X, et al. Research on electrical power quality disturbance recognition method based on edge computing and LightGBM[C]// 2021 3rd International Conference on Computer Science Communication and Network Security (CSCNS2021). 2022. DOI:10.1051/itmconf/20224501028.
[19] GAO Y, ZHANG H R, YU F, et al. Joint computation offloading and resource allocation for mobile-edge computing assisted ultra-dense networks[J]. Journal of Communications and Information Networks, 2022(1):96-106.
[20] TONG M L, WANG X X, LI S, et al. Joint offloading decision and resource allocation in mobile edge computing-enabled satellite-terrestrial network[J]. Symmetry, 2022,14(3). DOI: 10.3390/sym14030564.
[21] LU L P, ZHOU J. Research on mining of applied mathematics educational resources based on edge computing and data stream classification[J]. Mobile Information Systems, 2021. DOI: 10.1155/2021/5542718.
[22] ZHANG X J, HUANG C, GU D W, et al. Privacy-preserving statistical analysis over multi-dimensional aggregated data in edge computing-based smart grid systems[J]. Journal of Systems Architecture, 2022,127.
[23] SEO Y S, HUH J H. GUI-based software modularization through module clustering in edge computing based loT environments[J]. Journal of Ambient Intelligence and Humanized Computing, 2022,13(3):1625-1639.
[24] QIU H, LI T. Auction method to prevent bid-rigging strategies in mobile blockchain edge computing resource allocation[J]. Future Generations Computer Systems, 2022,128:1-15. DOI: 10.1016/j.future.2021.09.031.
[25] XUE F, HAI Q R, DONG T T, et al. A deep reinforcement learning based hybrid algorithm for efficient resource scheduling in edge computing environment[J]. Information Sciences, 2022,608:362-374.
[26] LI J, YANG Z P, WANG X W, et al. Task offloading mechanism based on federated reinforcement learning in mobile edge computing[J]. Digital Communications and Networks, 2023,9(2):492-504.
[27] BARANWAL G, KUMAR D, VIDYARTHI D P. BARA: A blockchain-aided auction-based resource allocation in edge computing enabled industrial internet of things[J]. Future Generations Computer Systems: FGCS, 2022,135:333-347.