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Complex, Resilient and Smart Systems

  • Dániel TokodyEmail author
  • József Papp
  • László Barna Iantovics
  • Francesco Flammini
Chapter
Part of the Advanced Sciences and Technologies for Security Applications book series (ASTSA)

Abstract

“Cyber-Physical Systems or “smart” systems are co-engineered interacting networks of physical and computational components. These systems will provide the foundation of our critical infrastructure, form the basis of emerging and future smart services, and improve our quality of life in many areas.” (National Institute of Standards and Technology: Cyber-physical systems. [Online]. Available: https://www.nist.gov/el/cyber-physical-systems. Accessed 31 Dec 2017, 2017). The concept of Smartness has been increasingly used as a marketing catchphrase. This study seeks to explain that smartness can be a serious indicator which can help to describe the machine intelligence level of different devices, systems or networks weighted by, among others, the usability index. The present study aims to summarize the implementation of complex, resilient and smart system on the level of devices, systems and complex system networks. The research should consider a smart device as a single agent, the system as a multi-agent system, and the network of complex systems has been envisaged as an ad hoc multi-agent system (Farid AM: Designing multi-agent systems for resilient engineering systems. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 9266, pp 3–8, 2015) organised in a network. The physical incarnations of this latter could be, for example, the subsystems of a smart city. In order to determine the smartness of a certain system, the Machine Intelligence Quotient (MIQ) (Iantovics LB, Gligor A, Georgieva V: Detecting outlier intelligence in the behavior of intelligent coalitions of agents. In: 2017 IEEE congress on evolutionary computation (CEC), pp 241–248, 2017; Park H-J, Kim BK, Lim KY: Measuring the machine intelligence quotient (MIQ) of human-machine cooperative systems. IEEE Trans Syst Man Cybern – Part A Syst Humans 31(2):89–96, 2001; Park HJ, Kim BK, Lim GY: Measuring machine intelligence for human-machine coop-erative systems using intelligence task graph. In: Proceedings 1999 IEEE/RSJ international conference on intelligent robots and systems. Human and environment friendly robots with high intelligence and emotional quotients (Cat. No.99CH36289), vol 2, pp 689–694, 1999; Ozkul T: Cost-benefit analyses of man-machine cooperative systems by assesment of machine intelligence quotient (MIQ) gain. In: 2009 6th international symposium on mechatronics and its applications, pp 1–6, 2009), Usability Index (UI) (Li C, Ji Z, Pang Z, Chu S, Jin Y, Tong J, Xu H, Chen Y: On usability evaluation of human – machine interactive Interface based on eye movement. In: Long S, Dhillon BS (eds) Man-machine-environment system engineering: proceedings of the 16th international conference on MMESE. Springer, Singapore, pp 347–354, 2016; Szabó G: Usability of machinery. In: Arezes P (ed) Advances in safety management and human factors: proceedings of the AHFE 2017 international conference on safety management and human factors, July 17–21, 2017, The Westin Bonaventure Hotel, Los Angeles, California, USA. Springer International Publishing, Cham, pp 161–168, 2018; Aykin N (ed): Usability and internationalization of information technology. Lawrence Erlbaum Associates, Inc., Publishers, Mahwah, 2005) and Usability Index of Machine (UIoM), Environmental Performance Index (Hsu A et al: Global metrics for the environment. In: The environmental performance index ranks countries’ performance on high-priority environmental issues. Yale University, New Haven, 2016) of Machine (EPIoM) indexes will be considered. The quality of human life is directly influenced by the intelligence and smart design of machines (Farid AM: Designing multi-agent systems for resilient engineering systems. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 9266, pp 3–8, 2015; Liouane Z, Lemlouma T, Roose P, Weis F, Liouane Z, Lemlouma T, Roose P, Weis F, Neu HMAG: A genetic neural network approach for unusual behavior prediction in smart home. In: Madureira AM, Abraham A, Gamboa D, Novais P (eds) Advances in intelligent systems and computing, vol 2016. Springer International Publishing AG, Porto, pp 738–748, 2017). Smartness of systems have an indispensable role to play in enabling the overall resilience of the combined cyber-physical system.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Dániel Tokody
    • 1
    Email author
  • József Papp
    • 1
  • László Barna Iantovics
    • 2
  • Francesco Flammini
    • 3
  1. 1.Doctoral School on Safety and Security SciencesÓbuda UniversityBudapestHungary
  2. 2.Petru Maior UniversityTirgu MuresRomania
  3. 3.Linnaeus UniversityVäxjöSweden

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