Cyber-Physical Cloud Computing Systems and Internet of Everything

The Industry 4.0 is experiencing massive transition in terms of performance and cost efficiency due to the emergence of Disruptive technologies. This applies in particular to smart computing on a big scale such as Cyber Physical Systems (CPS), Cloud Computing, the Internet of Things (IoTs), the Internet of Everything (IoE), Robotics (Mechatronics), Renewable Energy Systems, Autonomous vehicles and Intelligent Cities/Devices. CPS integrates networks, computations and physical processes to control process, respond, give feedback and adapt to changing conditions in the real time. Success of Industry 4.0 is confronted by disruptive CPS difficulties regulated by IoTs and IoE; integration with machine learning functionalities, cloud computing and growing but challenging concentration on the main fields of Big Data Analytics, Virtualization, and Automation. The chapter synthesizes existing literature to highlight drastic alterations that Industry 4.0 will apply on manufacturing systems and processes and explores the various domains revolving around CPS, challenges, applications and the ecosystem. It discusses studies and ways of implementing solutions that have been simplified using standards and systematic methods of investigation.

[1]  Vasja Roblek,et al.  A Complex View of Industry 4.0 , 2016 .

[2]  Abe Zeid,et al.  Interoperability in Smart Manufacturing: Research Challenges , 2019, Machines.

[3]  Nitesh V. Chawla,et al.  Data Mining for Imbalanced Datasets: An Overview , 2005, The Data Mining and Knowledge Discovery Handbook.

[4]  Rainer Ruggaber,et al.  ATHENA - Advanced Technologies for Interoperability of Heterogeneous Enterprise Networks and their Applications , 2006 .

[5]  Yang Lu,et al.  Industry 4.0: A survey on technologies, applications and open research issues , 2017, J. Ind. Inf. Integr..

[6]  Aji Gautama Putrada,et al.  Cyber physical system: Paper survey , 2014, 2014 International Conference on ICT For Smart Society (ICISS).

[7]  Georges Hébrail,et al.  Sliding HyperLogLog: Estimating Cardinality in a Data Stream over a Sliding Window , 2010, 2010 IEEE International Conference on Data Mining Workshops.

[8]  João Gama,et al.  Issues in evaluation of stream learning algorithms , 2009, KDD.

[9]  Rahim Rahmani,et al.  Enabling distributed intelligence assisted Future Internet of Things Controller (FITC) , 2018 .

[10]  Xuemin Shen,et al.  Autonomous Channel Switching: Towards Efficient Spectrum Sharing for Industrial Wireless Sensor Networks , 2016, IEEE Internet of Things Journal.

[11]  Feng Xia,et al.  A Secured Health Care Application Architecture for Cyber-Physical Systems , 2011, ArXiv.

[12]  Franco Zambonelli,et al.  Looking ahead in pervasive computing: Challenges and opportunities in the era of cyber-physical convergence , 2012, Pervasive Mob. Comput..

[13]  Shirish Tatikonda,et al.  SystemML: Declarative machine learning on MapReduce , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[14]  Bernd J. Krämer Evolution of Cyber-Physical Systems: A Brief Review , 2014 .

[15]  YangQuan Chen,et al.  Optimal Observation for Cyber-physical Systems: A Fisher-information-matrix-based Approach , 2009 .

[16]  Didier Stricker,et al.  Visual Computing as a Key Enabling Technology for Industrie 4.0 and Industrial Internet , 2015, IEEE Computer Graphics and Applications.

[17]  Dirk Schaefer,et al.  Software-defined cloud manufacturing for industry 4.0 , 2016 .

[18]  Dianhui Wang,et al.  A decentralized training algorithm for Echo State Networks in distributed big data applications , 2016, Neural Networks.

[19]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[20]  Syed Imran Shafiq,et al.  Virtual Engineering Factory: Creating Experience Base for Industry 4.0 , 2016, Cybern. Syst..

[21]  Bernhard Rumpe,et al.  Models for digitalization , 2015, Software & Systems Modeling.

[22]  D. Ivanov,et al.  Schedule coordination in cyber-physical supply networks Industry 4.0 , 2016 .

[23]  Ragunathan Rajkumar A Cyber–Physical Future , 2012, Proceedings of the IEEE.

[24]  Graham Cormode,et al.  Approximating Data with the Count-Min Sketch , 2012, IEEE Software.

[25]  Jennifer Widom,et al.  Models and issues in data stream systems , 2002, PODS.

[26]  Jay Lee,et al.  A Cyber Physical Interface for Automation Systems—Methodology and Examples , 2015 .

[27]  Siddhartha Kumar Khaitan,et al.  Design Techniques and Applications of Cyberphysical Systems: A Survey , 2015, IEEE Systems Journal.

[28]  Alexander J. Smola,et al.  Hokusai - Sketching Streams in Real Time , 2012, UAI.

[29]  Manfred Broy,et al.  Cyber-Physical Systems: Imminent Challenges , 2012, Monterey Workshop.

[30]  Yu Peng,et al.  Review on cyber-physical systems , 2017, IEEE/CAA Journal of Automatica Sinica.

[31]  Hong Chen,et al.  Applications of Cyber-Physical System: A Literature Review , 2017 .

[32]  M. Fromhold-Eisebith Cyber Physical Systems in Smart Cities - Mastering Technological, Economic, and Social Challenges , 2017 .

[33]  YangQuan Chen,et al.  Optimal mobile actuator/sensor network motion strategy for parameter estimation in a class of cyber physical systems , 2009, 2009 American Control Conference.

[34]  Lech Jóźwiak Embedded Computing Technology for Highly-demanding Cyber-physical Systems , 2015 .

[35]  Shirina Samreen,et al.  Cyber Physical Systems for Smart Cities Development , 2018, International Journal of Engineering & Technology.

[36]  Ioan Ungurean,et al.  An IoT architecture for things from industrial environment , 2014, 2014 10th International Conference on Communications (COMM).

[37]  Svein G. Johnsen,et al.  The ATHENA Interoperability Framework , 2007, IESA.

[38]  Erik Hofmann,et al.  Industry 4.0 and the current status as well as future prospects on logistics , 2017, Comput. Ind..

[39]  Z. Allam,et al.  On big data, artificial intelligence and smart cities , 2019, Cities.

[40]  P K Sowell The C4ISR Architecture Framework: History, Status, and Plans for Evolution , 2006 .

[41]  Edward A. Lee Computing needs time , 2009, CACM.

[42]  M. Balamurugan,et al.  Unique Sense: Smart Computing Prototype for Industry 4.0 Revolution with IOT and Bigdata Implementation Model , 2016, ArXiv.

[43]  Alexander Hall,et al.  HyperLogLog in practice: algorithmic engineering of a state of the art cardinality estimation algorithm , 2013, EDBT '13.

[44]  Ali Asghar Ghaemi,et al.  A cyber-physical system approach to smart city development , 2017, 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC).

[45]  Syed Imran Shafiq,et al.  Virtual Engineering Object (VEO): Toward Experience-Based Design and Manufacturing for Industry 4.0 , 2015, Cybern. Syst..

[46]  Hans-Georg Kemper,et al.  Application-Pull and Technology-Push as Driving Forces for the Fourth Industrial Revolution , 2014 .

[47]  YangQuan Chen,et al.  Optimal Observation for Cyber-physical Systems , 2009 .

[48]  Thomas Seidl,et al.  MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering , 2010, WAPA.

[49]  Gerhard Weikum,et al.  Distributed hash sketches: Scalable, efficient, and accurate cardinality estimation for distributed multisets , 2009, TOCS.

[50]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[51]  Gábor Lugosi,et al.  Prediction, learning, and games , 2006 .