Cyber-physical system

A cyberphysical system (CPS) is a computer system in which a mechanism is controlled or monitored by computer-based algorithms. In cyber-physical systems, physical and software components are deeply intertwined, able to operate on different spatial and temporal scales, exhibit multiple and distinct behavioral modalities, and interact with each other in ways that change with context.[1] Examples of CPS include smart grid, autonomous automobile systems, medical monitoring, industrial control systems, robotics systems, and automatic pilot avionics.[2]

CPS involves transdisciplinary approaches, merging theory of cybernetics, mechatronics, design and process science.[3][4] The process control is often referred to as embedded systems. In embedded systems, the emphasis tends to be more 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.[5]

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

Overview

Unlike more 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. The notion is closely tied to concepts of robotics and sensor networks with intelligence mechanisms proper of computational intelligence leading the pathway. Ongoing advances in science and engineering improve the link between computational and physical elements by means of intelligent mechanisms, increasing the adaptability, autonomy, efficiency, functionality, reliability, safety, and usability of cyber-physical systems.[6] This will broaden the potential of cyber-physical systems in several directions, including: intervention (e.g., collision avoidance); precision (e.g., robotic surgery and nano-level manufacturing); operation in dangerous or inaccessible environments (e.g., search and rescue, firefighting, and deep-sea exploration); coordination (e.g., air traffic control, war fighting); efficiency (e.g., zero-net energy buildings); and augmentation of human capabilities (e.g. in healthcare monitoring and delivery).[7]

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, 4G, EDGE, Bluetooth for interconnecting devices to either the Internet, or to other devices
  • High-level programming languages that enable rapid development of mobile CPS node software, such as Java,[8] C#, or JavaScript
  • Readily available application distribution mechanisms, such as Google Play 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 locally available, one common mechanism for rapid implementation of smartphone-based mobile cyber-physical system nodes utilizes the network connectivity to link the mobile system with either a server or a cloud environment, enabling complex processing tasks that are impossible under local resource constraints.[9] Examples of mobile cyber-physical systems include applications to track and analyze CO2 emissions,[10] detect traffic accidents, insurance telematics[11] and provide situational awareness services to first responders,[12][13] measure traffic,[14] and monitor cardiac patients.[15]

Examples

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 relay the processed information to a central node. Other types of CPS include smart grid,[16] autonomous automotive systems, medical monitoring, process control systems, distributed robotics, and automatic pilot avionics.

A real-world example of such a system is the Distributed Robot Garden at MIT in which a team of robots tend a garden of tomato plants. This system combines distributed sensing (each plant is equipped with a sensor node monitoring its status), navigation, manipulation and wireless networking.[17]

A focus on the control system aspects of CPS that pervade 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 that are not well quantified, such as cyber security,[18] 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 then used for calculating fastest routes for a given time of the day.[19]

CPS are also used in electric grids to perform advanced control, especially in the smart grids context to enhance the integration of distributed renewable generation. Special remedial action scheme are needed to limit the current flows in the grid when wind farm generation is too high. Distributed CPS are a key solution for this type of issues [20]

In industry domain, the cyber-physical systems empowered by Cloud technologies have led to novel approaches[21][22][23] 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.

Design

A challenge in the development of embedded and cyber-physical systems is the large differences in the design practice between the various engineering disciplines involved, such as software and mechanical engineering. Additionally, as of today there is no "language" in terms of design practice that is common to all the involved disciplines in CPS. Today, in a marketplace where rapid innovation is assumed to be essential, engineers from all disciplines need to be able to explore system designs 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 cooperate without enforcing new tools or design methods.[24] Results from the MODELISAR project show that this approach is viable by proposing a new standard for co-simulation in the form of the Functional Mock-up Interface.

Importance

The US National Science Foundation (NSF) has identified cyber-physical systems as a key area of research.[25] Starting in late 2006, the NSF and other United States federal agencies sponsored several workshops on cyber-physical systems.[26][27][28][29][30][31][32][33][34]

gollark: Thus bad.
gollark: I want to write code in this way, I think this way is *better*, and it won't let me.
gollark: I think my style is better than the Go one.
gollark: For "fast stupider web-y Python" Go somewhat works, but I don't think it's generally very good.
gollark: I think languages which do are generally better than ones which don't.

See also

References
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  3. 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".
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  5. 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 Agriculture for Life, Life for Agriculture. 6: 73–79.
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  7. "Cyber-physical systems". Program Announcements & Information. The National Science Foundation, 4201 Wilson Boulevard, Arlington, Virginia 22230, USA. 2008-09-30. Retrieved 2009-07-21.
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  10. 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 habits," in Proceedings of the 27th international conference on Human factors in computing systems. ACM, 2009, pp. 1043–1052.
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  12. Thompson, C.; White, J.; Dougherty, B.; Schmidt, D. C. (2009). "Optimizing Mobile Application Performance with Model–Driven Engineering" (PDF). Software Technologies for Embedded and Ubiquitous Systems. Lecture Notes in Computer Science. 5860. p. 36. doi:10.1007/978-3-642-10265-3_4. ISBN 978-3-642-10264-6.
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  19. "CarTel [MIT Cartel]". cartel.csail.mit.edu. 2011. Archived from the original on August 11, 2007. Retrieved November 16, 2011.
  20. Liu, R.; Srivastava, A. K.; Bakken, D. E.; Askerman, A.; Panciatici, P. (November–December 2017). "Decentralized State Estimation and Remedial Control Action for Minimum Wind Curtailment Using Distributed Computing Platform". IEEE Transactions on Industry Applications. 53 (6): 5915. doi:10.1109/TIA.2017.2740831. OSTI 1417238.
  21. A. W. 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.
  22. Wu, D.; Rosen, D.W.; Wang, L.; Schaefer, D. (2014). "Cloud-Based Design and Manufacturing: A New Paradigm in Digital Manufacturing and Design Innovation" (PDF). Computer-Aided Design. 59: 1–14. doi:10.1016/j.cad.2014.07.006.
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  28. "NSF Cyber-Physical Systems Summit". Archived from the original on 2009-05-12. Retrieved 2008-08-01.
  29. "National Workshop on High-Confidence Automotive Cyber-Physical Systems". Archived from the original on 2008-08-27. Retrieved 2008-08-03.
  30. "National Workshop on Composable and Systems Technologies for High-Confidence Cyber-Physical Systems". Retrieved 2008-08-04.
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  33. "Bridging the Cyber, Physical, and Social Worlds". Archived from the original on 2012-07-16. Retrieved 2011-02-25.
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Further reading
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