Cyber-physical system

cyber-physical (also styled cyberphysical ) system ( CPS ) is a mechanism that is controlled or monitored by computer-based algorithms, tightly integrated with the Internet and its users. In cyber-physical systems, physical and software components are deeply intertwined, each operating on different spatial and temporal scales, exhibiting multiple and distinct behavioral modalities, and interacting with each other in a myriad of ways that change with context . [1] Examples of CPS include smart grid , autonomous automotive systems, medical monitoring ,process control systems , robotic systems, and automatic pilot avionics. [2]

CPS involves transdisciplinary approaches, merging theory of cybernetics , mechatronics , design and process science. [3] [4] [5] The process is often referred to as embedded systems . In embedded systems, the emphasis is on the computational elements, and less on an intense link between the computational and physical elements. CPS is also similar to the Internet of Things (IoT), sharing the same basic architecture; Nevertheless, CPS presents a higher combination and coordination between physical and computational elements. [6]

Precursors of cyber-physical systems can be found in different aerospace , automotive , chemical processes , civil infrastructure, energy, healthcare , manufacturing , transportation , entertainment , and consumer appliances . [2]


Unlike most traditional embedded systems, a full-fledged CPS is typically designed as a network of interacting elements with physical input and output instead of as standalone devices. [7] The concept is closely related to the concepts of robotics and sensor networks with intelligence and proper mechanisms of computational intelligence leading the pathway. The following is a discussion of the importance of intelligent mechanisms, dramatically increasing the adaptability, autonomy, efficiency, functionality, reliability, safety, and usability of cyber-physical systems. [8]This will broaden the potential of cyber-physical systems in several dimensions, including: intervention (eg, collision avoidance); precision (eg, robotic surgery and nano-level manufacturing); operations in dangerous or inaccessible environments (eg, search and rescue, firefighting, deep-sea exploration , coordination (eg, air traffic control, war fighting), efficiency (eg, zero-net eg, healthcare monitoring and delivery). [9]

Mobile cyber-physical systems

Mobile cyber physical systems, in which the physical system under study has inherent mobility, are a prominent subcategory of cyber-physical systems. Examples of mobile physical systems include mobile robotics and electronics transported by humans or animals. The rise in popularity of smartphones has increased interest in the area of ​​mobile cyber-physical systems. Smartphone platforms make ideal mobile cyber-physical systems for a number of reasons, including:

  • Significant computational resources, such as processing capability, local storage
  • Multiple sensory input / output devices, such as touch screens, cameras, GPS chips, speakers, microphone, light sensors, proximity sensors
  • Multiple communication mechanisms, such as WiFi, 3G, EDGE, Bluetooth for interconnecting devices to the Internet, or to other devices
  • CPS node software, such as Java, [10] Objective C, JavaScript, ECMAScript or C #
  • Readily-available application distribution mechanisms, Android Market App Store and Apple App Store
  • End-user maintenance and upkeep, including frequent re-charging of the battery

For tasks that require more resources than are available locally, one common mobile device for mobile-based mobile device use of the mobile network are impossible under local resource constraints. [14] Examples of mobile cyber-physical systems include applications to track and analyze CO 2 emissions, [12] detect traffic accidents, insurance telematics [13] and provide situational awareness services to first responders, [14] [15] measure traffic, [16] and monitor cardiac patients. [17]


Common applications of CPS typically fall under sensor-based communication-enabled autonomous systems. For example, many wireless sensor networks monitor some aspect of the environment and a central node. Other types of CPS include smart grid , [18] autonomous automotive systems, medical monitoring, distributed robotics, and automated pilot avionics.

A real-world example of such a system is the Distributed Robot Garden at MIT in which a team of robots tend to a garden of tomato plants. This system combines distributed sensing, navigation, manipulation and wireless networking. [19]

A focus on the control system aspects of CPS that pervades critical infrastructure can be found in the efforts of the Idaho National Laboratory and collaborators researching resilient control systems . This effort takes a holistic approach to next generation design, and considers the resilience aspects of such as cyber security, [20] human interaction and complex interdependencies.

Another example is MIT’s ongoing CarTel project where a fleet of taxis work by collecting real-time traffic information in the Boston area. Together with historical data, this information is used for calculating fastest routes for a given time of the day. [21]

In industry domain, the cyber-physical systems empowered by cloud technologies-have led to novel approaches [22] [23] [24] That paved the path to Industry 4.0 as the European Commission IMC-AESOP project with partners Such As Schneider Electric , SAP , Honeywell , Microsoft etc. Demonstrated.

Cyber-physical models for future manufacturing-With a cyber-physical system, a “coupled-model” approach has been developed. [25]The coupled model is a twinned machine that operates in the cloud computing platform and simulates the health condition with an integrated knowledge of data. The coupled model first constructs a digital image from the early design stage. System information and physical knowledge are based on product design, based on which is a model for future analysis. Initial parameters may be statistically generalized and they can be estimated using the estimation method. The simulation model can be considered as a mirrored image of the real machine, finally, With the help of cloud computing technology, the combined model is more easily available for machine managers. These features pave the way to implementingcyber manufacturing . [26] [27]


A challenge in the development of embedded and cyber-physical systems is the wide differences in the design practice between the various engineering disciplines involved, such as software and mechanical engineering. Additionally, there is no “language” in terms of design practice that is common to all involved disciplines in CPS. Today, in a marketplace where rapid innovation is essential, engineers from all disciplines need to be able to explore systems collaboratively, allocating responsibilities to software and physical elements, and analyzing trade-offs between them. Recent advances show that coupling disciplines by using co-simulation will allow disciplines to work with new tools. [28] Results from the MODELISARThis is a new approach to co-simulation in the form of the Functional Mock-up Interface .

Can be done based on the 5C architecture (connection, conversion, cyber, cognition, and configuration). [29]In the “Connection” level, devices can be designed to self-connect and self-sensing for its behavior. In the “Conversion” level, data from self-connected devices and sensors are measuring the features of critical issues with self-aware capabilities, machines can use the self-aware information to self-predict its potential issues. In the “Cyber” level, each machine is creating its own “twin” by using these features and further characterizes the machine based on a “Time-Machine” methodology. The established “twin” in the cyber space can perform self-comparison for peer-to-peer performance for further synthesis. In the “Cognition” level, the outcomes of self-assessment and self-evaluation will be presented to users based on an “infographic” meaning to show the content and context of the potential issues. In the “Configuration” level, the machine or production system can be reconfigured based on the priority and risk criteria to achieve resilient performance.[30]

The original twin model idea came from [31] , in which a physical operation was coupled with a virtual operation by means of an intelligent reasoning agent. The detailed version of this concept is presented in [32] .


The US National Science Foundation (NSF) has identified cyber-physical systems as a key area of ​​research. [33] Starting in late 2006, the NSF and other United States federal agencies sponsored several workshops on cyber-physical systems. [34] [35] [36] [37] [38] [39] [40] [41] [42]

See also

  • Indoor positioning system
  • Industry 4.0
  • Signal flow graph
  • Internet of Things


  1. Jump up^ “US National Science Foundation,Cyber-Physical Systems (CPS)”
  2. ^ Jump up to:b Khaitan et al. ” Design Techniques and Applications of Cyber Physical Systems: A Survey “, IEEE Systems Journal, 2014.
  3. Jump up^ Hancu, O .; Maties, V .; Balan, R .; Stan, S. (2007). “Mechatronic approach for design and control of a hydraulic 3-dof parallel robot”. The 18th International DAAAM Symposium, “Intelligent Manufacturing & Automation: Focus on Creativity, Responsibility and Ethics of Engineers” .
  4. Jump up^ Lee, EA, Seshia, SA:Introduction to Embedded Systems – A Cyber-Physical Systems Approach., 2011.
  5. Jump up^ Suh, SC, Carbon, JN, Eroglu, AE:Applied Cyber-Physical Systems. Springer, 2014.
  6. Jump up^ Rad, Ciprian-Radu; Hancu, Olimpiu; Takacs, Ioana-Alexandra; Olteanu, Gheorghe (2015). “Smart Monitoring of Potato Crop: A Cyber-Physical System Architecture Model in the Field of Precision Agriculture”. Conference for Agriculture, Life for Agriculture . 6 : 73-79.
  7. Jump up^ Lee, Edward (January 23, 2008). “Cyber ​​Physical Systems: Design Challenges” . University of California, Berkeley Technical Report No. UCB / EECS-2008-8 . Retrieved 2008-06-07 .
  8. Jump up^ C.Alippi: Intelligence for Embedded Systems . Springer Verlag, 2014, 283pp,ISBN 978-3-319-05278-6.
  9. Jump up^ “Cyber-physical systems” . Program Announcements & Information . The National Science Foundation, 4201 Wilson Boulevard, Arlington, Virginia 22230, USA. 2008-09-30 . Retrieved 2009-07-21 .
  10. Jump up^ “Virtual Machine for Running Java Applications on CPS” . Retrieved 2012-04-12 .
  11. Jump up^ White, Jules ; Clarke, S .; Dougherty, B .; Thompson, C .; Schmidt, D. “R & D Challenges and Solutions for Mobile Cyber-Physical Applications and Supporting Internet Services” (PDF) . Springer Journal of Internet Services and Applications . Retrieved 2011-02-21 .
  12. Jump up^ J. Froehlich, T. Dillahunt, P. Klasnja, J. Mankoff, S. Consolvo, B. Harrison, and J. Landay, “UbiGreen: Investigating a mobile tool for tracking and supporting green transportation,” in Proceedings of the 27th international conference on Human factors in computing systems. ACM, 2009, pp. 1043-1052.
  13. Jump up^ P. Handel, I. Skog, J. Wahlstrom, F. Bonawide, R. Welsh, J. Ohlsson, and M. Ohlsson: Insurance telematics: Intelligent Transportation Systems Magazine, IEEE, vol.6, no.4, pp. 57-70, winter 2014, doi: 10.1109 / MITS.2014.2343262
  14. Jump up^ Thompson, C .; White, J .; Dougherty, B .; Schmidt, DC (2009). “Optimizing Mobile Application Performance with Model-Driven Engineering”. Software Technologies for Embedded and Ubiquitous Systems (PDF) . Reading Notes in Computer Science. 5860 . p. 36. doi : 10.1007 / 978-3-642-10265-3_4 . ISBN  978-3-642-10264-6 .
  15. Jump up^ Jones, WD (2001). “Forecasting traffic flow”. IEEE Spectrum . 38 : 90. doi :10.1109 / 6.901153 .
  16. Jump up^ Rose, G. (2006). “Mobile Phones as Traffic Probes: Practices, Prospects and Issues”. Transport Reviews . 26 (3): 275-291. doi : 10.1080 / 01441640500361108 .
  17. Jump up^ Leijdekkers, P. (2006). “Personal Heart Monitoring and Rehabilitation System using Smart Phones”. 2006 International Conference on Mobile Business . p. 29. doi : 10.1109 / ICMB.2006.39 . ISBN  0-7695-2595-4 .
  18. Jump up^ S. Karnouskos: Cyber-Physical Systems in the Smart Grid (PDF, 79 kB). In:Industrial Informatics (INDIN), 2011 9th International IEEE Conference on, July 2011. Retrieved 20 Apr 2014.
  19. Jump up^ “The Distributed Robotics Garden” . . 2011 . Retrieved November 16, 2011 .
  20. Jump up^ Loukas, George (June 2015). Cyber-Physical Attacks A growing invisible threat . Oxford, UK: Butterworh-Heinemann (Elsevier). p. 65. ISBN  9780128012901 .
  21. Jump up^ “CarTel [MIT Cartel]” . . 2011 . Retrieved November 16, 2011 .
  22. Jump up^ AW Colombo, T. Bangemann, S. Karnouskos, J. Delsing, P. Stluka, R. Harrison, F. Jammes, and J. Lastra: Industrial Cloud-based Cyber-Physical Systems: The IMC-AESOP Approach . Springer Verlag, 2014,ISBN 978-3-319-05623-4.
  23. Jump up^ Wu, D .; Rosen, DW; Wang, L .; Schaefer, D. (2014). “Cloud-Based Design and Manufacturing: A New Paradigm in Digital Manufacturing and Design Innovation”. Computer-Aided Design . 59 : 1-14. doi : 10.1016 / j.cad.2014.07.006 .
  24. Jump up^ Wu, D., Rosen, DW, & Schaefer, D. (2014). Cloud-Based Design and Manufacturing: Status and Promise. In: Schaefer, D. (Ed): Cloud-Based Design and Manufacturing: A Service-Oriented Product Development Paradigm for the 21st Century, Springer, London, UK, pp.1-24.
  25. Jump up^ Lee, Jay; Bagheri, Behrad; Kao, Hung-An (2014). “Recent Advances and Trends of Cyber-Physical Systems and Big Data Analytics in Industrial Informatics” . IEEE Int. Conference on Industrial Informatics (INDIN) 2014 .
  26. Jump up^ Lee, Jay; Lapira, Edzel; Bagheri, Behrad; Kao, Hung-an. “Recent advances and trends in big data environment predictive manufacturing . “Manufacturing Letters . 1 (1): 38-41. doi : 10.1016 / j.mfglet.2013.09.005 .
  27. Jump up^ Predictive manufacturing system
  28. Jump up^ J.Fitzgerald, PG Larsen, M. Verhoef (Eds.): Collaborative Design for Embedded Systems: Co-modeling and Co-simulation . Springer Verlag, 2014,ISBN 978-3-642-54118-6.
  29. Jump up^ “IMS_CPS – IMS Center” . .
  30. Jump up^ Lee, Jay; Bagheri, Behrad; Kao, Hung-An (January 2015). “A Cyber-Physical Systems Architecture for Industry 4.0-based Manufacturing Systems”. Manufacturing Letters . 3 : 18-23. doi : 10.1016 / j.mfglet.2014.12.001 .
  31. Jump up^ Lee, Jay (January 1998). “Teleservice engineering in manufacturing: challenges and opportunities”. International Journal of Machine Tools and Manufacture . 38 : 901-910. doi : 10.1016 / S0890-6955 (97) 00135-1 .
  32. Jump up^ Lee, Jay (1993). “Analysis of machine degradation using a neural network based pattern discrimination model”. Journal of Manufacturing Systems . 12 : 379-387. doi : 10.1016 / 0278-6125 (93) 90306-E .
  33. Jump up^ Wolf, Wayne (November 2007). “The Good News and the Bad News (Embedded Computing Column”. IEEE Computer . 40 (11):. 104-105 doi :10.1109 / MC.2007.404 .
  34. Jump up^ “NSF Workshop On Cyber-Physical Systems” . Archived from the original on 2008-05-17 . Retrieved 2008-06-09 .
  35. Jump up^ “Beyond SCADA: Networked Embedded Control for Cyber ​​Physical Systems” . Archived from the original on January 17, 2009 . Retrieved 2008-06-09 .
  36. Jump up^ “NSF Cyber-Physical Systems Summit” . Archived from the original on 2009-05-12 . Retrieved 2008-08-01 .
  37. Jump up^ “National Workshop on Automotive High-Confidence Cyber-Physical Systems” . Archived from the original on 2008-08-27 . Retrieved 2008-08-03 .
  38. Jump up^ “National Workshop on Composable and Systems Technologies for High-Confidence Cyber-Physical Systems,” . Retrieved 2008-08-04 .
  39. Jump up^ “National Workshop on High-Confidence Software Platforms for Cyber-Physical Systems (HCSP-CPS),” . Retrieved 2008-08-04 .
  40. Jump up^ “New Research Directions for Future Cyber-Physical Energy Systems” . Retrieved 2009-06-05 .
  41. Jump up^ “Bridging the Cyber, Physical, and Social Worlds” . Archived from the original on 2012-07-16 . Retrieved 2011-02-25 .
  42. Jump up^ “NIST Foundations for Innovation in Cyber-Physical Systems Workshop”. Retrieved 2012-02-08 .

Leave a Reply

Your email address will not be published. Required fields are marked *

Copyright 2019
Shale theme by Siteturner