Advertisement

What the Design Theory of Social-Cyber-Physical Systems Must Describe, Explain and Predict?

Abstract

An intense shift from traditional mechatronics systems to cyber-physical systems has been taking place over the last two decades. The former systems, which integrate mechanical, electronics, computing, control and situated reasoning components, are typically implemented as closed, predefined, controlled and deterministic systems. The latter systems are characterized by open system boundaries, large functional and structural complexities, self-learning and -reasoning capabilities, partial autonomy, context-driven adaptability, and decentralized decision making. As the latest trend, they are getting more extensively embedded in the fabric of society. There exists no dedicated design theory that would explain how to conceptualize, design and realize this family of non-linear systems. In this paper, concentrating on the changing place and role of information, first an overview of the milestones of the overall physical, biological, social and technological evolution is given. Then, the distinguishing characteristics of cyber-physical systems are analysed, the current transition towards social-cyber-physical systems is considered, and the need for a comprehensive design theory for this family of systems is explained. As examples of the aspects which the design theory of social-cyber-physical systems should describe, explain, and predict, the phenomenon of: (a) aggregative complexity, (b) emergent attributes/behaviour, (c) compositional synergy, (d) multi-abstraction-based specification, (e) dynamic scalability, (f) multi-modal prototyping, and (g) integrity verification and behaviour validation are discussed and the issues of handling them in design are addressed. The main proposition is that there is an urgent need for a multi-disciplinary research in this novel domain of interest in order to facilitate a deeper disciplinary understanding and to allow the development of specific design methodologies and computational support tools.

Keywords

Design Theory Mechatronic System Aggregative Complexity Integrity Verification Digital Computing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Goyal P (2012) Information physics—towards a new conception of physical reality. Inf 3:567–594Google Scholar
  2. 2.
    Wheeler JA (1989) It from bit. In: Proceedings of the 3rd International Symposium on the Foundations of Quantum Mechanics, TokyoGoogle Scholar
  3. 3.
    Deutsch D (2003) It from Qubit. In: Barrow J, Davies P, Harper C (eds) Science and ultimate reality. Cambridge University Press, Cambridge, pp 1–16Google Scholar
  4. 4.
    Vedral V (2012) Information and physics. Inf 3:219–223Google Scholar
  5. 5.
    Auletta G (2010) A paradigm shift in biology? Inf 1:28–59Google Scholar
  6. 6.
    Biro JC (2011) Biological information—definitions from a biological perspective. Inf 2:117–139Google Scholar
  7. 7.
    del Moral R, González M, Navarro J, Marijuán PC (2011) From genomics to scientomics: expanding the bioinformation paradigm. Inf 2:651–671Google Scholar
  8. 8.
    Yan X-S (2011) Information science: its past, present and future. Inf 2:510–527Google Scholar
  9. 9.
    Beckerman LP (2000) Application of complex systems science to systems engineering. Syst Eng 3(2):96–102CrossRefGoogle Scholar
  10. 10.
    Wu FJ, Kao YF, Tseng YC (2011) From wireless sensor networks towards cyber physical systems. Pervasive Mob Comput 7(4):397–413CrossRefGoogle Scholar
  11. 11.
    Horváth I, Gerritsen BHM (2012) Cyber-physical system: concepts, technologies and manifestation. In: Proceedings of the TMCE, Karlsruhe, pp 1–16, 2012Google Scholar
  12. 12.
    Isermann R (2008) Mechatronic systems—innovative products with embedded control. Control Eng Pract 16:14–29CrossRefGoogle Scholar
  13. 13.
    Harashima F, Tomizuka M (1996) Mechatronics—“what it is, why and how?”. IEEE/ASME Trans Mechatron 1:1–2Google Scholar
  14. 14.
    Bishop RH, Ramasubramanian MK (2002) What is mechatronics?. In: Overview of mechatronics, CRC Press LLC, Boca Raton, pp 1–11Google Scholar
  15. 15.
    Kyura N, Oho H (1996) Mechatronics—an industrial perspective. IEEE/ASME Trans Mechatron 1:10–15CrossRefGoogle Scholar
  16. 16.
    Bhave A, Garlan D, Krogh B, Rajhans A, Schmerl B (2010) Augmenting software architectures with physical components. In: Proceedings of the embedded real time software and systems conference, pp 19–21, 2010Google Scholar
  17. 17.
    Marwedel P (2001) Embedded and cyber-physical systems in a nutshell, Knowledge center article, DAC.COM, 2001, pp 1–21Google Scholar
  18. 18.
    Hewitt C (1985) The challenge of open systems: current logic programming methods may be insufficient for developing the intelligent systems of the future. Byte 10(4):223–242Google Scholar
  19. 19.
    Stankovic JA (1988) Misconceptions about real-time computing: a serious problem for next-generation systems. Comput 21(10):10–19CrossRefGoogle Scholar
  20. 20.
    Benveniste A, Berry G (1991) The synchronous approach to reactive and real-time systems. Proc IEEE 79(9):1270–1282CrossRefGoogle Scholar
  21. 21.
    Baillieul J, Antsaklis P (2007) Control and communication challenges in networked real-time systems. Proc IEEE 95(1):9–28CrossRefGoogle Scholar
  22. 22.
    Aoyama M, Tanabe H (2011) A design methodology for real-time distributed software architecture based on the behavioral properties and its application to advanced automotive software. In: Proceedings of the 18th Asia-Pacific software engineering conference, IEEE, pp 211–218, 2011Google Scholar
  23. 23.
    Chong C-Y, Kumar SP (2003) Sensor networks: evolution, opportunities, and challenges. Proc IEEE 91(8):1247–1256CrossRefGoogle Scholar
  24. 24.
    Sztipanovits J, Koutsoukos X, Karsai G, Kottenstette N, Antsaklis P, Gupta V, Goodwine B, Baras J, Wang S (2012) Toward a science of cyber–physical system integration. Proc IEEE 100(1):29–44CrossRefGoogle Scholar
  25. 25.
    Kim K-D, Kumar PR (2012) Cyber-physical systems: a perspective at the Centennial. In: Proceedings of the IEEE, vol 100. pp 1287–1308, 2012Google Scholar
  26. 26.
    Ma J, Wen J, Huang R, Huang B (2011) Cyber-individual meets brain informatics. IEEE Intell Syst 26(5):30–37CrossRefGoogle Scholar
  27. 27.
    Lee EA (2010) CPS Foundations. In: Proceedings of the 47th design automation conference, ACM, pp 737–742, 2010Google Scholar
  28. 28.
    Ren C-J, Huang H-B, Jin S (2008) Specification of agent in complex adaptive system. In: Proceedings of the international symposium on computer science and computational technology, 2, pp 210–216Google Scholar
  29. 29.
    Ning H, Liu H (2012) Cyber-physical-social based security architecture for future internet of things. Adv Internet Things 2:1–7CrossRefGoogle Scholar
  30. 30.
    Lee EA (2007) Computing needs time. Commun ACM 52(5):70–79CrossRefGoogle Scholar
  31. 31.
    Dillon TS, Zhuge H, Wu C, Singh J, Chang E (2011) Web-of-things framework for cyber–physical systems, Concurrency Comput: Pract Experıence 23(9):905–923Google Scholar
  32. 32.
    Work D, Bayen A, Jacobson Q (2008) Automotive cyber physical systems in the context of human mobility. In: Proceedings of national workshop on high-confidence automotive cyber-physical systems, Troy, MI, 3–4 April 2008Google Scholar
  33. 33.
    Lee I, Sokolsky O (2010) Medical cyber physical systems. In: Proceedings of DAC ‘10, ACM, Anaheim, CA, pp. 743–748, 13–18 June 2010Google Scholar
  34. 34.
    Martín HJA, de Lope J, Maravall D (2009) Adaptation, anticipation and rationality in natural and artificial systems: computational paradigms mimicking nature. Nat Comput 8(4):757–775CrossRefMathSciNetGoogle Scholar
  35. 35.
    Herrmann K, Mühl G, Geihs K (2005) Self-management: the solution to complexity or just another problem? IEEE Distrib Syst Online 6(1):1–17CrossRefGoogle Scholar
  36. 36.
    Kay JJ (2000) Ecosystems as self-organizing holarchic open systems: narratives and the second law of thermodynamics. In: Handbook of ecosystem theories and management, CRC Press, Lewis Publishers, pp 135–160, 2000Google Scholar
  37. 37.
    Biamino G (2012) A semantic model for socially aware objects. Adv Internet Things 2(7):47–55CrossRefGoogle Scholar
  38. 38.
    Arthur WB (1993) Why do things become more complex? Sci Am 268(5):92CrossRefGoogle Scholar
  39. 39.
    Wolfram S (1986) Approaches to complexity engineering. Physica D(22):385–399Google Scholar
  40. 40.
    Amaral LAN, Ottino JM (2004) Complex networks: completing the framework for the study of complex systems. Eur Phys J 38:47–162CrossRefGoogle Scholar
  41. 41.
    Fisk D (2004) Engineering complexity. Inter-disciplinary Science Reviews 29(2):151–161Google Scholar
  42. 42.
    Dent EB (1999) Complexity science: a worldview shift. Emergence 1:5–9CrossRefGoogle Scholar
  43. 43.
    Delic K, Dum R (2006) On the emerging future of complexity sciences. ACM Ubiquity 7(10):1Google Scholar
  44. 44.
    Yaneer B-Y (2002) Multi-scale complex systems analysis and enlightened evolutionary engineering. NECSI, Cambridge, MA, pp 25–29Google Scholar
  45. 45.
    Saunders PT, Ho MW (1976) On the increase in complexity in evolution. J Theor Biol 63:375–384CrossRefGoogle Scholar
  46. 46.
    Johnson C (2005) What are emergent properties and how do they affect the engineering of complex systems? Reliab Eng Syst Saf 91(12):1475–1481CrossRefGoogle Scholar
  47. 47.
    Liu J, Hu BC (2008) On emergent complex behaviour, self-organised criticality and phase transitions in multi-agent systems: autonomy oriented computing perspectives. Int J Model Ident Control 3(1):3–16CrossRefGoogle Scholar
  48. 48.
    Herrmann K, Mühl G, Geihs K (2005) Self-management: the solution to complexity or just another problem? IEEE Distrib Syst Online 6(1):1541–4922CrossRefGoogle Scholar
  49. 49.
    Kramer J, Magee J (2007) Self-managed systems: an architectural challenge. In: Future of software engineering, IEEE Computer Society, Washington, DC, pp 259–268, 2007Google Scholar
  50. 50.
    Steels L (1991) Towards a theory of emergent functionality. In: From animals to animats: 1st international conference on simulation of adaptive behaviour, Paris, France, pp 451–461, 1991Google Scholar
  51. 51.
    Sole R, Ferrer-Cancho R, Montoya J, Valverde S (2002) Selection, tinkering and emergence in complex networks. Complexity 8(1):20–31CrossRefMathSciNetGoogle Scholar
  52. 52.
    Gossler G, Sifakis J (2005) Composition for component-based modelling. Sci Comput Program 55(1–3):161–183CrossRefMathSciNetGoogle Scholar
  53. 53.
    Brooks A, Kaupp T, Makarenko A, Williams S, Orebäck A (2005) Towards component-based robotics. In: Proceedings of the international conference intelligent robots and systems, IEEE, pp 163–168, 2005Google Scholar
  54. 54.
    Arzén K-E, Bicchi A, Dini G, Hailes S (2007) A component-based approach to the design of networked control systems. Eur J Control 13(2–3):261–279CrossRefGoogle Scholar
  55. 55.
    Lee EA, Xiong Y (2001) System-level types for component-based design, In: Embedded software, Springer, Berlin, pp 237–253, 2001Google Scholar
  56. 56.
    Sztipanovits J (2007) Composition of cyber‐physical systems, In: Proceedings of the 14th IEEE international conference and workshop on the engineering of computer‐based systems, pp 3–6, 2007Google Scholar
  57. 57.
    Brazier FMT, Cornelissen F, Jonker CM, Treur J (2000) Compositional specification and reuse of a generic co-operative agent model. Int J Coop Inf Syst 9:171–207CrossRefGoogle Scholar
  58. 58.
    Graham S, Baliga G, Kumar P (2009) Abstractions, architecture, mechanisms, and a middleware for networked control. IEEE Trans Autom Control 54(7):1490–1503CrossRefMathSciNetGoogle Scholar
  59. 59.
    Willems JC (2007) The behavioral approach to open and interconnected systems. IEEE Control Syst Mag 27(6):46–99CrossRefMathSciNetGoogle Scholar
  60. 60.
    Wan J, Suo H, Yan H, Liu J (2011) A general test platform for cyber-physical systems: unmanned vehicle with wireless sensor network navigation. In: Proceedings of the international conference on advances in engineering, Nanjing, China, pp 1–5, 2011Google Scholar
  61. 61.
    Gorissen D, Couckuyt I, Demeester P, Dhaene T, Crombecq K (2010) A surrogate modelling and adaptive sampling toolbox for computer based design. J Mach Learn Res 11(7):2051–2055Google Scholar
  62. 62.
    Atkinson C, Kühne T (2003) Model-driven development: a metamodeling foundation. IEEE Softw 20(5):36–41CrossRefGoogle Scholar
  63. 63.
    Selic B (2003) The pragmatics of model-driven development. IEEE Softw 20(5):19–25CrossRefGoogle Scholar
  64. 64.
    Thacker RA et al (2010) Automatic abstraction for verification of cyber-physical systems. In: Proceedings of international conference on cyber-physical systems, pp 12–21, 2010Google Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Faculty of Industrial Design EngineeringDelft University of TechnologyDelftThe Netherlands

Personalised recommendations