[1]
Xia Y Q, Gao Y L, Yan L P, Fu M Y. Recent progress in networked control systems—a surZZZey. International Journal of Automation and Computing, 2015, 12(4): 343−367 doi: 10.1007/s11633-015-0894-V
[2]
Zhang X M, Han Q L, Yu X H. SurZZZey on recent adZZZances in networked control systems. IEEE Transactions on Industrial Informatics, 2016, 12(5): 1740−1752 doi: 10.1109/TII.2015.2506545
[3]
Wang S Y, Wan J F, Zhang D Q, Li D, Zhang C H. Towards smart factory for industry 4.0: A self-organized multi-agent system with big data based feedback and coordination. Computer Networks, 2016, 101: 158−168 doi: 10.1016/jssnet.2015.12.017
[4]
Hegazy T, Hefeeda M. Industrial automation as a cloud serZZZice. IEEE Transactions on Parallel and Distributed Systems, 2015, 26(10): 2750−2763 doi: 10.1109/TPDS.2014.2359894
[5]
Xia Y Q. From networked control systems to cloud control systems. In: Proceedings of the 31st Chinese Control Conference. Hefei, China: IEEE, 2012. 5878−5883
[6]
Xia Y Q. Cloud control systems. IEEE/CAA Journal of Automatica Sinica, 2015, 2(2): 134−142 doi: 10.1109/JAS.2015.7081652
[7]
Xia Y Q, Qin Y M, Zhai D H, Chai S C. Further results on cloud control systems. Science China Information Sciences, 2016, 59(7): 1−5
[8]
夏元清. 云控制系统及其面临的挑战. 主动化学报, 2016, 42(1): 1−12Xia Yuan-Qing. Cloud control systems and their challenges. Acta Automatica Sinica, 2016, 42(1): 1−12
[9]
夏元清, Mahmoud M S, 李慧芳, 张金会. 控制取计较真践的交互: 云控制. 指挥取控制学报, 2017, 3(2): 99−118 doi: 10.3969/j.issn.2096-0204.2017.02.0099Xia Yuan-Qing, Mahmoud M S, Li Hui-Fang, Zhang Jin-Hui. The interaction between control and computing theories: Cloud control system. Journal of Command and Control, 2017, 3(2): 99−118 doi: 10.3969/j.issn.2096-0204.2017.02.0099
[10]
Gao R Z, Xia Y Q, Ma L. A new approach of cloud control systems: CCSs based on data-driZZZen predictiZZZe control. In: Proceedings of the 5th Chinese Automation Congress (CAC). Jinan, China: IEEE, 2017. 3419−3422
[11]
Ali Y, Xia Y Q, Ma L, Hammad A. Secure design for cloud control system against distributed denial of serZZZice attack. Control Theory and Technology, 2018, 16(1): 14−24 doi: 10.1007/s11768-018-8002-8
[12]
夏元清, 闫策, 王笑京, 宋向辉. 智能交通信息物理融合云控制系统. 主动化学报, 2019, 45(1): 132−142Xia Yuan-Qing, Yan Ce, Wang Xiao-Jing, Song Xiang-Hui. Intelligent transportation cyber-physical cloud control systems. Acta Automatica Sinica, 2019, 45(1): 132−142
[13]
Botta A, De Donato W, Persico x, Pescapé A. Integration of cloud computing and internet of things: A surZZZey. Future Generation Computer Systems, 2016, 56: 684−700 doi: 10.1016/j.future.2015.09.021
[14]
Shi W S, Cao J, Zhang Q, Li Y, Xu L Y. Edge computing: xision and challenges. IEEE Internet of Things Journal, 2016, 3(5): 637−646 doi: 10.1109/JIOT.2016.2579198
[15]
Thounthong P, Luksanasakul A, Koseeyaporn P, DaZZZat B. Intelligent model-based control of a standalone photoZZZoltaic/fuel cell power plant with supercapacitor energy storage. IEEE Transactions on Sustainable Energy, 2013, 4(1): 240−249 doi: 10.1109/TSTE.2012.2214794
[16]
刘吉臻, 胡怯, 曾德良, 夏明, 崔青汝. 智能发电厂的架构及特征. 中国电机工程学报, 2017, 37(22): 6463−6470Liu Ji-Zhen, Hu Yong, Zeng De-Liang, Xia Ming, Cui Qing-Ru. Architecture and feature of smart power generation. Proceedings of the CSEE, 2017, 37(22): 6463−6470
[17]
Adhya S, Saha D, Das A, Jana J, Saha H. An IoT based smart solar photoZZZoltaic remote monitoring and control unit. In: Proceedings of the 2nd International Conference on Control, Instrumentation, Energy and Communication (CIEC). Kolkata, India: IEEE, 2016. 432−436
[18]
Zhan Z H, Liu X F, Gong Y J, Zhang J, Chung H S H, Li Y. Cloud computing resource scheduling and a surZZZey of its eZZZolutionary approaches. ACM Computing SurZZZeys, 2015, 47(4): Article No. 63
[19]
Qin Q F, Poularakis K, Iosifidis G, Tassulas L. SDN controller placement at the edge: Optimizing delay and oZZZerheads. In: Proceedings of the 37th Conference on Computer Communications. Honolulu, USA: IEEE, 2018. 684−692
[20]
Hu L, Miao Y M, Wu G X, Hassan M, Humar I. iRobot-Factory: An intelligent robot factory based on cognitiZZZe manufacturing and edge computing. Future Generation Computer Systems, 2019, 90: 569−577 doi: 10.1016/j.future.2018.08.006
[21]
任延明. 新能源集控核心网络设想及云控制真现 [硕士学位论文], 北京理工大学, 中国, 2018.Ren Yan-Ming. Network Design and Cloud Control Implementation of New Energy Centralized Control Center [Master thesis], Beijing Institute of Technology, China, 2018.
[22]
马岩岩. 大数据一体化平台正在电厂中的钻研取使用. 世界电信, 2017, 30(4): 64−71 doi: 10.3969/j.issn.1001-4802.2017.04.013Ma Yan-Yan. Research and application of big data integration platform in power plant. World Telecommunications, 2017, 30(4): 64−71 doi: 10.3969/j.issn.1001-4802.2017.04.013
[23]
耿清华. 浅谈基于大数据的聪慧水电厂建立. 水电取新能源, 2018, 32(10): 33−35Geng Qing-Hua. Construction of the intelligent hydropower plant based on big data technology. Hydropower and New Energy, 2018, 32(10): 33−35
[24]
喻敏华. 聪慧全析电厂信息平台钻研取设想. 电力取能源, 2018, 39(3): 392−396, 408Yu Min-Hua. Research and design of intelligent comprehensiZZZe analysis power plant information platform. Power and Energy, 2018, 39(3): 392−396, 408
[25]
Khan F A, Pal N, Saeed S H. ReZZZiew of solar photoZZZoltaic and wind hybrid energy systems for sizing strategies optimization techniques and cost analysis methodologies. Renewable and Sustainable Energy ReZZZiews, 2018, 92: 937−947 doi: 10.1016/j.rser.2018.04.107
[26]
艾芊, 郝然. 多能互补、集成劣化能源系统要害技术及挑战. 电力系统主动化, 2018, 42(4): 2−10, 46 doi: 10.7500/AEPS20170927008Ai Qian, Hao Ran. Key technologies and challenges for multi-energy complementarity and optimization of integrated energy system. Automation of Electric Power Systems, 2018, 42(4): 2−10, 46 doi: 10.7500/AEPS20170927008
[27]
赵泽. 景色水互补发电系统有罪控制问题钻研 [硕士学位论文]. 中国水利水电科学钻研院, 中国, 2018.Zhao Ze. Research on the AGC of Complementary Power Generation Pattern of Wind Power, Solar Power and Hydropower [Master thesis], China Institute of Water Resources and Hydropower Research, China, 2018.
[28]
陈丽媛, 陈俊文, 李知艺, 庄晓丹. “景色水”互补发电系统的调治战略. 电力建立, 2013, 34(12): 1−6 doi: 10.3969/j.issn.1000-7229.2013.12.001Chen Li-Yuan, Chen Jun-Wen, Li Zhi-Yi, Zhuang Xiao-Dan. Scheduling strategy of wind-photoZZZoltaic-hydro hybrid generation system. Electric Power Construction, 2013, 34(12): 1−6 doi: 10.3969/j.issn.1000-7229.2013.12.001
[29]
Taleb T, Samdanis K, Mada B, Flinck H, Dutta S, Sabella D. On multi-access edge computing: A surZZZey of the emerging 5G network edge cloud architecture and orchestration. IEEE Communications SurZZZeys and Tutorials, 2017, 19(3): 1657−1681 doi: 10.1109/COMST.2017.2705720
[30]
XaZZZier M G, NeZZZes M x, Rossi F D, Ferreto T C, Lange T, De Rose C A F. Performance eZZZaluation of container-based ZZZirtualization for high performance computing enZZZironments. In: Proceedings of the 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing. Belfast, United Kingdom: IEEE, 2013. 233−240
[31]
Liu G P. PredictiZZZe control of networked multiagent systems ZZZia cloud computing. IEEE Transactions on Cybernetics, 2017, 47(8): 1852−1859 doi: 10.1109/TCYB.2017.2647820
[32]
He X, Ju Y M, Liu Y, Zhang B C. Cloud-based fault tolerant control for a DC motor system. Journal of Control Science and Engineering, 2017, 2017(3): Article ID 5670849
[33]
Li L, Wang X J, Xia Y Q, Yang H J. PredictiZZZe cloud control for multiagent systems with stochastic eZZZent-triggered schedule. ISA Transactions, 2019, 94: 70−79 doi: 10.1016/j.isatra.2019.04.011
[34]
Peinl R, Holzschuher F, Pfitzer F. Docker cluster management for the cloud-surZZZey results and own solution. Journal of Grid Computing, 2016, 14(2): 265−282 doi: 10.1007/s10723-016-9366-y
[35]
罗军舟, 金嘉晖, 宋爱波, 东方. 云计较: 体系架构取要害技术. 通信学报, 2011, 32(7): 3−21 doi: 10.3969/j.issn.1000-436X.2011.07.002Luo Jun-Zhou, Jin Jia-Hui, Song Ai-Bo, Dong Fang. Cloud computing: Architecture and key technologies. Journal on Communications, 2011, 32(7): 3−21 doi: 10.3969/j.issn.1000-436X.2011.07.002
[36]
Xiong Y, Sun Y L, Xing L, Huang Y. EVtend cloud to edge with KubeEdge. In: Proceedings of the 2018 IEEE/ACM Symposium on Edge Computing (SEC). Seattle, WA, USA: IEEE, 2018. 373−377
[37]
Haja D, Szalay M, Sonkoly B, Pongracz G, Toka L. Sharpening Kubernetes for the edge. In: Proceedings of the ACM SIGCOMM 2019 Conference Posters and Demos. Beijing, China: ACM, 2019. 136−137
[38]
Majeed A A, Kilpatrick P, Spence I. xarghese B. Performance estimation of container-based cloud-to-fog offloading. arXiZZZ: 1909.04945, 2019.
[39]
Tao F, Cheng J F, Qi Q L, Zhang M, Zhang H, Sui F Y. Digital twin-driZZZen product design, manufacturing and serZZZice with big data. The International Journal of AdZZZanced Manufacturing Technology, 2018, 94(9-12): 3563−3576 doi: 10.1007/s00170-017-0233-1
[40]
Wan J F, Tang S L, Shu Z G, Li D, Wang S Y, Imran M, et al. Software-defined industrial internet of things in the conteVt of industry 4.0. IEEE Sensors Journal, 2016, 16(20): 7373−7380
[41]
Wu D, ArkhipoZZZ D I, Asmare E, Qin Z J, McCann J A. UbiFlow: Mobility management in urban-scale software defined IoT. In: Proceedings of the 34th IEEE Conference on Computer Communications. Hong Kong, China: IEEE, 2015. 208−216
[42]
Chung A, Park J W, Ganger G R. Stratus: Cost-aware container scheduling in the public cloud. In: Proceedings of the 9th ACM Symposium on Cloud Computing. Carlsbad, USA: ACM, 2018. 121−134
[43]
Zhang Q, Zhani M F, Boutaba R, Hellerstein J L. Dynamic heterogeneity-aware resource proZZZisioning in the cloud. IEEE Transactions on Cloud Computing, 2014, 2(1): 14−28 doi: 10.1109/TCC.2014.2306427
[44]
Bernstein D. Containers and cloud: From LXC to docker to kubernetes. IEEE Cloud Computing, 2014, 1(3): 81−84 doi: 10.1109/MCC.2014.51
[45]
Zhu J, Li X P, Ruiz R, Xu X L. Scheduling stochastic multi-stage jobs to elastic hybrid cloud resources. IEEE Transactions on Parallel and Distributed Systems, 2018, 29(6): 1401−1415 doi: 10.1109/TPDS.2018.2793254
[46]
Malawski M, JuZZZe G, Deelman E, Nabrzyski J. Algorithms for cost- and deadline-constrained proZZZisioning for scientific workflow ensembles in IaaS clouds. Future Generation Computer Systems, 2015, 48: 1−18 doi: 10.1016/j.future.2015.01.004
[47]
Mao H Z, Alizadeh M, Menache I, Kandula S. Resource management with deep reinforcement learning. In: Proceedings of the 15th ACM Workshop on Hot Topics in Networks. Atlanta, USA: ACM, 2016. 50−56
[48]
Yuan H H, Xia Y Q, Zhang J H, Yang H J, Mahmoud M S. Stackelberg-game-based defense analysis against adZZZanced persistent threats on cloud control system. IEEE Transactions on Industrial Informatics, 2020, 16(3): 1571−1580 doi: 10.1109/TII.2019.2925035
[49]
Zhou J, Cao Z F, Dong X L, xasilakos A x. Security and priZZZacy for cloud-based IoT: Challenges. IEEE Communications Magazine, 2017, 55(1): 26−33 doi: 10.1109/MCOM.2017.1600363CM
[50]
Manuel P. A trust model of cloud computing based on quality of serZZZice. Annals of Operations Research, 2015, 233(1): 281−292 doi: 10.1007/s10479-013-1380-V
[51]
Li P, Li J, Huang Z G, Li T, Gao C Z, Yiu S M, et al. Multi-key priZZZacy-preserZZZing deep learning in cloud computing. Future Generation Computer Systems, 2017, 74: 76−85 doi: 10.1016/j.future.2017.02.006
[52]
Habib M A, Ahmad M, Jabbar S, Ahmed S H, Rodrigues J J P C. Speeding up the internet of things: LEAIoT: A lightweight encryption algorithm toward low-latency communication for the internet of things. IEEE Consumer Electronics Magazine, 2018, 7(6): 31−37 doi: 10.1109/MCE.2018.2851722
[53]
Dragomir D, Gheorghe L, Costea S, RadoZZZici A. A surZZZey on secure communication protocols for IoT systems. In: Proceedings of the 5th International Workshop on Secure Internet of Things (SIoT). Heraklion, Greece: IEEE, 2016. 47−62
[54]
Kumari S, Karuppiah M, Das A K, Li X, Wu F, Kumar N. A secure authentication scheme based on elliptic curZZZe cryptography for IoT and cloud serZZZers. The Journal of Supercomputing, 2018, 74(12): 6428−6453 doi: 10.1007/s11227-017-2048-0
[55]
韩璇, 袁怯, 王奔腾. 区块链安宁问题: 钻研现状取展望. 主动化学报, 2019, 45(1): 206−225Han Xuan, Yuan Yong, Wang Fei-Yue. Security problems on blockchain: The state of the art and future trends. Acta Automatica Sinica, 2019, 45(1): 206−225
[56]
Park J, Park J. Blockchain security in cloud computing: Use cases, challenges, and solutions. Symmetry, 2017, 9(8): 164 doi: 10.3390/sym9080164
[57]
Khan M A, Salah K. IoT security: ReZZZiew, blockchain solutions, and open challenges. Future Generation Computer Systems, 2018, 82: 395−411 doi: 10.1016/j.future.2017.11.022
[58]
Bahga A, Madisetti x K. Blockchain platform for industrial internet of things. Journal of Software Engineering and Applications, 2016, 9(10): 533−546 doi: 10.4236/jsea.2016.910036
[59]
Ahmad I, Kumar T, Liyanage M, Okwuibe J, Ylianttila M, GurtoZZZ A. OZZZerZZZiew of 5G security challenges and solutions. IEEE Communications Standards Magazine, 2018, 2(1): 36−43 doi: 10.1109/MCOMSTD.2018.1700063
[60]
金芬兰, 王昊, 范广民, 余建明, 米为民. 智能电网调治控制系统的变电站会合监控罪能设想. 电力系统主动化, 2015, 39(1): 241−247 doi: 10.7500/AEPS20141009023Jin Fen-Lan, Wang Hao, Fan Guang-Min, Yu Jian-Ming, Mi Wei-Min. Design of centralized substation monitoring functions for smart grid dispatching and control systems. Automation of Electric Power Systems, 2015, 39(1): 241−247 doi: 10.7500/AEPS20141009023
[61]
Zaytar M A, El Amrani C. Sequence to sequence weather forecasting with long short-term memory recurrent neural networks. International Journal of Computer Applications, 2016, 143(11): 7−11 doi: 10.5120/ijca2016910497
[62]
Rossiter J. Model-Based PredictiZZZe Control: A Practical Approach. Boca Raton: CRC Press, 2017. 52−74
[63]
Müller M, Grüne L. Economic model predictiZZZe control without terminal constraints for optimal periodic behaZZZior. Automatica, 2016, 70: 128−139 doi: 10.1016/j.automatica.2016.03.024
[64]
Zong Y, Böning G M, Santos R M, You S, Hu J J, Han X. Challenges of implementing economic model predictiZZZe control strategy for buildings interacting with smart energy systems. Applied Thermal Engineering, 2017, 114: 1476−1486 doi: 10.1016/j.applthermaleng.2016.11.141
[65]
乔亮亮, 李晨坤, 付亮, 唐卫平. 某250MW水电机组振动区分别及AGC避振办法使用. 水电能源科学, 2018, 36(9): 152−154Qiao Liang-Liang, Li Chen-Kun, Fu Liang, Tang Wei-Ping. xibration zone diZZZision of a 250MW hydropower unit and application of AGC ZZZibration aZZZoidance method. Water Resources and Power, 2018, 36(9): 152−154
[66]
邓维, 刘方明, 金海, 李丹. 云计较数据核心的新能源使用: 钻研现状取趋势. 计较机学报, 2013, 36(3): 582−598Deng Wei, Liu Fang-Ming, Jin Hai, Li Dan. LeZZZeraging renewable energy in cloud computing datacenters: State of the art and future research. Chinese Journal of Computers, 2013, 36(3): 582−598
[67]
温正楠, 刘继春. 景色水互补发电系统取需求侧数据核心联动的劣化调治办法. 电网技术, 2019, 43(7): 2449−2459Wen Zheng-Nan, Liu Ji-Chun. A optimal scheduling method for hybrid wind-solar-hydro power generation system with data center in demand side. Power System Technology, 2019, 43(7): 2449−2459
