SCIENTIA SINICA Informationis, Volume 50 , Issue 2 : 195-220(2020) https://doi.org/10.1360/N112019-00005

The architecture and key technologies for an industrial Internet with synergy between the cloud and clients

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  • ReceivedJan 15, 2019
  • AcceptedApr 27, 2019
  • PublishedFeb 12, 2020


The fourth industrial revolution has been unveiled with the rapid development of Internet of things (IoT), cloud computing, and big data. The industrial Internet, as a highly cooperative and intelligence-sharing global network that connects entities, human beings, and the environment in smart manufacturing, is the core of this revolution. However, most current research on the industrial Internet is restricted to IoT, cloud computing, or big data, respectively. The synergy between the cloud and clients is currently at a very primary stage of sensing, connection, and knowledge, lacking a cloud-client-integrative architecture and key technologies that could meet the evolving requirements of networked smart production, including more complex objects to be sensed, more diversification of entities to be connected, faster data processing, and more intelligent feedback control. This paper first surveys some important research directions with respect to this research field and summarizes the research status and challenges. On this basis, a novel cloud-client integrative industrial Internet architecture and solutions for related key technologies are proposed. Then, the proposed technologies are demonstrated for some specific applications in the field of intelligent manufacturing. Finally, the prospects for cloud-client-integrative industrial Internet research are discussed and concluded.

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本文的撰写得到了江苏省网络与信息安全重点实验室的 金嘉晖、张竞慧、沈典、方效林和单冯等博士的帮助, 特此表示感谢


[1] Liu Y H. Internet of Everything for New Industrial Revolution. Beijing: Tsinghua University Press, 2016. Google Scholar

[2] Hegazy T, Hefeeda M. Industrial Automation as a Cloud Service. IEEE Trans Parallel Distrib Syst, 2015, 26: 2750-2763 CrossRef Google Scholar

[3] Goldin E, Feldman D, Georgoulas G, et al. Cloud computing for big data analytics in the process control industry. In: Proceedings of the 25th Mediterranean Conference on Control and Automation, Valletta, 2017. 1373--1378. Google Scholar

[4] Higuera-Toledano M T, Risco-Martin J L, Arroba P. Green Adaptation of Real-Time Web Services for Industrial CPS Within a Cloud Environment. IEEE Trans Ind Inf, 2017, 13: 1249-1256 CrossRef Google Scholar

[5] Huang G, Mei H. Synergy of cloud and clients: a new model of cloud computing. Commun CCF, 2016, 12: 20--22. Google Scholar

[6] Lee J, Bagheri B, Kao H A. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Lett, 2015, 3: 18-23 CrossRef Google Scholar

[7] Hu P. A system architecture for software-defined industrial Internet of things. In: Proceedings of IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB'15), Montreal, 2015. 1--5. Google Scholar

[8] Cruz D V, Almazán R S. E-gov 4.0: a literature review towards the new government. In: Proceedings of the 15th Annual International Conference on Digital Government Research, Aguascalientes, Mexico, 2014. 333--334. Google Scholar

[9] Garetti M, Taisch M. Sustainable manufacturing: trends and research challenges. Production Planning Control, 2012, 23: 83-104 CrossRef Google Scholar

[10] Bornschlegl M, Drechsel M, Kreitlein S, et al. A new approach to increasing energy efficiency by utilizing cyber-physical energy systems. In: Proceedings of the 11th Workshop on Intelligent Solutions in Embedded Systems, Pilsen, 2013. 1--6. Google Scholar

[11] Shrouf F, Ordieres J, Miragliotta G. Smart factories in industry 4.0: a review of the concept and of energy management approached in production based on the internet of things paradigm. In: Proceedings of IEEE International Conference on Industrial Engineering and Engineering Management, Selangor Darul Ehsan, 2014. 697--701. Google Scholar

[12] Medjaher K, Tobon-Mejia D A, Zerhouni N. Remaining Useful Life Estimation of Critical Components With Application to Bearings. IEEE Trans Rel, 2012, 61: 292-302 CrossRef Google Scholar

[13] Li J Q, Zhang S P, Yang L. Accurate RFID localization algorithm with particle swarm optimization based on reference tags. IFS, 2016, 31: 2697-2706 CrossRef Google Scholar

[14] Luo C W, Hong H D, Cheng L. Accuracy-aware wireless indoor localization: Feasibility and applications. J Network Comput Appl, 2016, 62: 128-136 CrossRef Google Scholar

[15] Luo C W, Cheng L, Chan M C. Pallas: Self-Bootstrapping Fine-Grained Passive Indoor Localization Using WiFi Monitors. IEEE Trans Mobile Comput, 2017, 16: 466-481 CrossRef Google Scholar

[16] Xi F F, Yu L, Tu X W. Framework on robotic percussive riveting for aircraft assembly automation. Adv Manuf, 2013, 1: 112-122 CrossRef Google Scholar

[17] Zhao Y M, Lin Y, Xi F F. Calibration-Based Iterative Learning Control for Path Tracking of Industrial Robots. IEEE Trans Ind Electron, 2015, 62: 2921-2929 CrossRef Google Scholar

[18] Pu Q F, Gupta S, Gollakota S, et al. Whole-home gesture recognition using wireless signals. In: Proceedings of the 19th Annual International Conference on Mobile Computing and Networking (MobiCom'13), Miami, 2013. 27--38. Google Scholar

[19] Ding H, Shangguan L F, Yang Z, et al. FEMO: a platform for free-weight exercise monitoring with RFIDs. In: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems (SenSys'15), Seoul, 2015. 141--154. Google Scholar

[20] Li T X, An C K, Tian Z, et al. Human sensing using visible light communication. In: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking (MobiCom'15), Paris, 2015. 331--344. Google Scholar

[21] Qian K, Wu C S, Yang Z, et al. PADS: passive detection of moving targets with dynamic speed using PHY layer information. In: Proceedings of the 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS'14), Hsinchu, 2014. 1--8. Google Scholar

[22] Zhou Z M, Yang Z, Wu C S, et al. Towards omnidirectional passive human detection. In: Proceedings of the IEEE INFOCOM 2013, Turin, 2013. 3057--3065. Google Scholar

[23] Huang D, Nandakumar R, Gollakota S. Feasibility and limits of Wi-Fi imaging. In: Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems (SenSys '14), Memphis, 2014. 266--279. Google Scholar

[24] Adib F, Katabi D. See through walls with Wi-Fi In: Proceedings of ACM SIGCOMM, Hong Kong, 2013. 75--86. Google Scholar

[25] Wang J, Vasisht D, Katabi D. RF-IDraw: virtual touch screen in the air using RF signals. In: Proceedings of ACM SIGCOMM, Chicago, 2014. 235--246. Google Scholar

[26] Ali K, Liu X, Wang W, et al. Keystroke recognition using Wi-Fi signals. In: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking (MobiCom'15), Paris, 2015. 90--102. Google Scholar

[27] Chen B, Yenamandra V, Srinivasan K. Tracking keystrokes using wireless signals. In: Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys'15), Florence, 2015. 31--44. Google Scholar

[28] Zhang C, Tabor J, Zhang J L, et al. Extending mobile interaction through near-field visible light sensing. In: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking (MobiCom'15), Paris, 2015. 345--357. Google Scholar

[29] EPCGlobal. Information Services Version 1.0 Specification. EPCglobal Ratified Standard. 2007. Google Scholar

[30] Koshizuka N, Sakamura K. Ubiquitous ID: Standards for Ubiquitous Computing and the Internet of Things. IEEE Pervasive Comput, 2010, 9: 98-101 CrossRef Google Scholar

[31] Hu P F, Ning H S, Qiu T. Fog Computing Based Face Identification and Resolution Scheme in Internet of Things. IEEE Trans Ind Inf, 2017, 13: 1910-1920 CrossRef Google Scholar

[32] Krikke J. T-Engine: Japan's ubiquitous computing architecture is ready for prime time. IEEE Pervasive Comput, 2005, 4: 4-9 CrossRef Google Scholar

[33] Deering S, Hinden R. Internet protocol, version 6 (IPv6) specification, 2017. Google Scholar

[34] Lin C, Xiong N X, Park J H. Dynamic power management in new architecture of wireless sensor networks. Int J Commun Syst, 2009, 22: 671-693 CrossRef Google Scholar

[35] Dunkels A. Full TCP/IP for 8-bit architectures. In: Proceedings of the 1st International Conference on Mobile Systems, Applications, and Services (MobiSys '03), San Francisco, 2003. Google Scholar

[36] Fonseca R, Ratnasamy S, Zhao J, et al. Beacon vector routing: scalable point-to-point routing in wireless sensor nets. In: Proceedings of the 2nd Symposium on Networked Systems Design and Implementation (NSDI '05), Boston, 2005. Google Scholar

[37] Hong S H, Song S M. Transmission of a Scheduled Message Using a Foundation Fieldbus Protocol. IEEE Trans Instrum Meas, 2008, 57: 268-275 CrossRef Google Scholar

[38] Li Q F, Jiang J. Evaluation of Foundation Fieldbus H1 Networks for Steam Generator Level Control. IEEE Trans Contr Syst Technol, 2011, 19: 1047-1058 CrossRef Google Scholar

[39] Cena G, Valenzano A. An improved CAN fieldbus for industrial applications. IEEE Trans Ind Electron, 1997, 44: 553-564 CrossRef Google Scholar

[40] Ma L, Yu F, Leung V C M. A new method to support UMTS/WLAN vertical handover using SCTP. IEEE Wireless Commun, 2004, 11: 44-51 CrossRef Google Scholar

[41] Yu F, Leung V C M. Mobility-based predictive call admission control and bandwidth reservation in wireless cellular networks. Comput Networks, 2002, 38: 577-589 CrossRef Google Scholar

[42] Yu F, Krishnamurthy V. Optimal Joint Session Admission Control in Integrated WLAN and CDMA Cellular Networks with Vertical Handoff. IEEE Trans Mobile Comput, 2007, 6: 126-139 CrossRef Google Scholar

[43] Baronti P, Pillai P, Chook V W C. Wireless sensor networks: A survey on the state of the art and the 802.15.4 and ZigBee standards. Comput Commun, 2007, 30: 1655-1695 CrossRef Google Scholar

[44] Bisdikian C. An overview of the Bluetooth wireless technology. IEEE Commun Mag, 2001, 39: 86-94 CrossRef Google Scholar

[45] Rrez J G, Villa-Medina J F, Nieto-Garibay A. Automated Irrigation System Using a Wireless Sensor Network and GPRS Module. IEEE Trans Instrum Meas, 2014, 63: 166-176 CrossRef Google Scholar

[46] Kim T, Kim I H, Sun Y J. Physical Layer and Medium Access Control Design in Energy Efficient Sensor Networks: An Overview. IEEE Trans Ind Inf, 2015, 11: 2-15 CrossRef Google Scholar

[47] Liang C C, Yu F R, Zhang X. Information-centric network function virtualization over 5g mobile wireless networks. IEEE Network, 2015, 29: 68-74 CrossRef Google Scholar

[48] Bangerter B, Talwar S, Arefi R. Networks and devices for the 5G era. IEEE Commun Mag, 2014, 52: 90-96 CrossRef Google Scholar

[49] Guey J C, Liao P K, Chen Y S, et al. On 5G radio access architecture and technology: industry perspectives. IEEE Wireless Commun. 2015, 22: 2-5 doi: 10.1109/MWC.2015.7306369. Google Scholar

[50] Andrews J G, Buzzi S, Choi W. What Will 5G Be?. IEEE J Sel Areas Commun, 2014, 32: 1065-1082 CrossRef Google Scholar

[51] Mahmud K, Wu G, Inoue M, et al. Mobility management by basic access network in MIRAI architecture for heterogeneous wireless systems. In: Proceedings of the Global Telecommunications Conference (GLOBECOM'02), Taipei, 2002. 1708--1712. Google Scholar

[52] Misra A, Roy A, Das S K. Information-Theory Based Optimal Location Management Schemes for Integrated Multi-System Wireless Networks. IEEE/ACM Trans Networking, 2008, 16: 525-538 CrossRef Google Scholar

[53] Knightson K, Morita N, Towle T. NGN architecture: generic principles, functional architecture, and implementation. IEEE Commun Mag, 2005, 43: 49-56 CrossRef Google Scholar

[54] Kirrane S, Mileo A, Decker S. Accesscontrol and the resource description framework: a survey. Semantic Web, 2017, 8: 311-352. Google Scholar

[55] Brakerski Z, Gentry C, Vaikuntanathan V. (Leveled) Fully Homomorphic Encryption without Bootstrapping. ACM Trans Comput Theor, 2014, 6: 1-36 CrossRef Google Scholar

[56] Su Y C, Chi Y C, Chen H Y. Data Erasing and Rewriting Capabilities of a Colorless FPLD Based Carrier-Reusing Transmitter. IEEE Photonics J, 2015, 7: 1-12 CrossRef ADS Google Scholar

[57] Eckersley P. How unique is your web browser? In: Proceedings of the 10th International Symposium on Privacy Enhancing Technologies (PETS'10), Berlin, 2010. 1--18. Google Scholar

[58] Mayer J R, Mitchell J C. Third-party web tracking: policy and technology. In: Proceedings of IEEE Symposium on Security and Privacy (SP'12), San Francisco, 2012. 413--427. Google Scholar

[59] Acar G, Rez M J A, Nikiforakis N, et al. FPDetective: dusting the web for finger printers. In: Proceedings of ACM SIGSAC Conference on Computer and Communications Security (CCS'13), Berlin, 2013. 1129--1140. Google Scholar

[60] Pang J, Greenstein B, Gummadi R, et al. 802.11 user fingerprinting. In: Proceedings of the 13th Annual International Conference on Mobile Computing and Networking (MobiCom'07), Montreal, 2007. 99--110. Google Scholar

[61] Yen T F, Xie Y L, Yu F, et al. Host fingerprinting and tracking on the web: privacy and security implications. In: Proceedings of the 19th Annual Network and Distributed System Security Symposium (NDSS'12), San Diego, 2012. Google Scholar

[62] Kleinmann A, Wool A. Automatic Construction of Statechart-Based Anomaly Detection Models for Multi-Threaded Industrial Control Systems. ACM Trans Intell Syst Technol, 2017, 8: 1-21 CrossRef Google Scholar

[63] Hermann M, Pentek T, Otto B. Design principles for industry 4.0 scenarios. In: Proceedings of the 49th Hawaii International Conference on System Sciences (HICSS), Koloa, 2016. 3928--3937. Google Scholar

[64] Wu X D, Zhu X Q, Wu G Q. Data mining with big data. IEEE Trans Knowl Data Eng, 2014, 26: 97-107 CrossRef Google Scholar

[65] Wang J M. Survey on Industrial Big Data. Big Data Research. 2017. Google Scholar

[66] Li J Q, Yu F R, Deng G Q. Industrial Internet: A Survey on the Enabling Technologies, Applications, and Challenges. IEEE Commun Surv Tutorials, 2017, 19: 1504-1526 CrossRef Google Scholar

[67] Xu L D, He W, Li S C. Internet of Things in Industries: A Survey. IEEE Trans Ind Inf, 2014, 10: 2233-2243 CrossRef Google Scholar

[68] Luo J Z, Jin J H, Song A B, et al. The architecture and key technologies of cloud computing. Journal on Communications, 2011, 7: 3--21. Google Scholar

[69] Tao F, Zhang L, Venkatesh V C, et al. Cloud manufacturing: a computing and service- oriented manufacturing model. In: Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, 2011. 225: 1969--1976. Google Scholar

[70] Li B H, Zhang L, Ren L, et al. Typical characteristics, technologies and applications of cloud manufacturing. Computer Integrated Manufacturing Systems, 2012, 18: 1345-1356. Google Scholar

[71] Botta A, de Donato W, Persico V. Integration of Cloud computing and Internet of Things: A survey. Future Generation Comput Syst, 2016, 56: 684-700 CrossRef Google Scholar

[72] Shi W S, Sun H, Cao J, et al. Edge computing: an emerging computing model for the internet of everything era. Journal of Computer Research and Development, 2017. Google Scholar

[73] Shi W S, Cao J, Zhang Q. Edge Computing: Vision and Challenges. IEEE Internet Things J, 2016, 3: 637-646 CrossRef Google Scholar

[74] Li J Q, Huang L X, Zhou Y M. Computation Partitioning for Mobile Cloud Computing in a Big Data Environment. IEEE Trans Ind Inf, 2017, 13: 2009-2018 CrossRef Google Scholar

[75] Eide E, Stack T, Regehr J, et al. Dynamic CPU management for real-time, middleware-based systems. In: Proceedings of Real-Time and Embedded Technology and Applications Symposium, Toronto, 2004. 286--295. Google Scholar

[76] Gill C D, Gossett J M, Corman D. Integrated Adaptive QoS Management in Middleware: A Case Study. Real-Time Syst, 2005, 29: 101-130 CrossRef Google Scholar

[77] Shankaran N, Koutsoukos X D, Schmidt D C, et al. Hierarchical control of multiple resources in distributed real-time and embedded systems. In: Proceedings of the 18th Euromicro Conference on Real-Time Systems (ECRTS'06), Dresden, 2006. 151--160. Google Scholar

[78] Wolfe V F, Dipippo L C, Cooper G. Real-time CORBA. IEEE Trans Parallel Distrib Syst, 2000, 11: 1073-1089 CrossRef Google Scholar

[79] Schantz R E, Loyall J P, Rodrigues C, et al. Flexible and adaptive QoS control for distributed real-time and embedded middleware. In: Proceedings of ACM/IFIP/USENIX International Middleware Conference (Middleware'03), Rio de Janeiro, 2003. 374--393. Google Scholar

[80] Krishnamurthy Y, Pyarali I, Gill C D, et al. The design and implementation of real-time CORBA 2.0: dynamic scheduling in TAO. In: Proceedings of the 10th IEEE Real-Time and Embedded Technology and Applications Symposium, Toronto, 2004. 121--129. Google Scholar

[81] Buttazzo G C. Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications. 2nd ed. Berlin: Springer, 2004. Google Scholar

[82] Cucinotta T, Mancina A, Anastasi G F. A Real-Time Service-Oriented Architecture for Industrial Automation. IEEE Trans Ind Inf, 2009, 5: 267-277 CrossRef Google Scholar

[83] Olfati-Saber R, Fax J A, Murray R M. Consensus and Cooperation in Networked Multi-Agent Systems. Proc IEEE, 2007, 95: 215-233 CrossRef Google Scholar

[84] Eidson J C, Lee E A, Matic S. Distributed Real-Time Software for Cyber-Physical Systems. Proc IEEE, 2012, 100: 45-59 CrossRef Google Scholar

[85] Lee J G, Gu J Y, Shin D H. Trust in unmanned driving system. In: Proceedings of the 10th Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI'15), Portland, 2015. 7-8. Google Scholar

[86] Jo K, Kim J, Kim D. Development of Autonomous Car-Part II: A Case Study on the Implementation of an Autonomous Driving System Based on Distributed Architecture. IEEE Trans Ind Electron, 2015, 62: 5119-5132 CrossRef Google Scholar

[87] Cruz T, Rosa L, Proenca J. A Cybersecurity Detection Framework for Supervisory Control and Data Acquisition Systems. IEEE Trans Ind Inf, 2016, 12: 2236-2246 CrossRef Google Scholar

[88] Barenji R V, Barenji A V, Hashemipour M. A multi-agent RFID-enabled distributed control system for a flexible manufacturing shop. Int J Adv Manuf Technol, 2014, 71: 1773-1791 CrossRef Google Scholar

[89] Alphonsus E R, Abdullah M O. A review on the applications of programmable logic controllers (PLCs). Renew Sustain Energy Rev, 2016, 60: 1185-1205 CrossRef Google Scholar

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