Integrated Estimation of a Cyber-Physical System’s Sustainability
Abstract
:1. Introduction
2. Materials and Methods
- Substantiation of relevance of the problem based on the literature review.
- Selection of the key indicators for assessment of CPS sustainability on the basis of considering the essence, structure, main properties of CPS, clarification of CPS sustainability, and requirements for key indicators.
- Reviewing existing methods for assessing CPS sustainability.
- Suggesting the most suitable approach to consider the variety of characteristics of CPS sustainability and the complexity of this term.
- Identification of the advantages of the proposed algorithm for assessing CPS sustainability and discussion.
3. Results
3.1. Comprehension of CPS Sustainability
3.1.1. Definition of CPS
3.1.2. Basic Elements of CPS
- Support technologies, which include: the Internet of Things (IoT), which provides “machine-human”, “machine-machine” communications, ubiquitous computing, embedded systems, cloud technologies, special network exchange technologies and fuzzy logic.
- Components of the physical environment, in particular, identification, measurement, data transmission, data processing tools, different interfaces, server equipment, diagnostic equipment, production equipment, including automation elements: sensors, control devices, actuators, robots, machine tools and intelligent systems.
- Components of the information environment such as Product Lifecycle Management System (PLM), Product Data Management System (PDM), Enterprise Resource Planning System (ERP), Manufacturing Execution System (MES), Supervisory Control and Data Acquisition (SCADA), Programmable Logic Controller (PLC), and OpenStack (a software package that implements the cloud platform functions).
3.1.3. Main Properties of CPS
3.1.4. Sustainability of CPS
3.2. Key Performance Indicators for Assessing CPS Sustainability
3.2.1. Requirements for Key CPS Sustainability Indicators
- Representativeness, for displaying the main characteristics of CPS sustainability.
- Measurability of the indicator.
- A limited number of indicators (the recommended number is not less than six and not more than twenty-five).
- Implementation of evaluation indicators for both information and physical components of the CPS.
- Setting indicators that characterize the CPS state as a whole, such a system-wide indicators reflecting the emergent properties of the system, that is, the CPS properties as a whole that are not inherent in its individual elements.
- Profitability—the costs of collecting information and evaluating CPS indicators should not exceed the possible effects of their use for management purposes.
- Complexity, which provides an opportunity to influence the determinants of the CPS sustainability indicators in real time.
3.2.2. Main CPS Sustainability Indicators
- -
- Number of possible failures (units)
- -
- Number of cyber-attacks (units)
- -
- The CPS energy efficiency ratio (index)
- -
- Speed of CPS operations (flops)
- -
- Fixed high-capacity network coverage (% of facilities)
- -
- Digital skills (% of employees)
- -
- Protection from Internet attacks (index)
- -
- Spotting vulnerabilities of information and physical components (index)
- -
- System manageability ratio (index)
- -
- Economic value-added (monetary unit).
3.3. Methodology for Estimating CPS Sustainability
3.3.1. Review of Existing Methods for Assessing CPS Sustainability
3.3.2. Integral CPS Sustainability Assessment
- (1)
- First of all, it is necessary to calculate the indicators’ growth rates for the analyzed period of time:
- (2)
- Construction of the matrix of reference indicators’ growth rates ratios (DN) A = :
- (3)
- Construction of the matrix of actual indicators’ growth rates ratios F = :
- (4)
- Construction of the matrix of coincidences of actual and reference indicators’ growth rates ratios C = :
- (5)
- Estimation of proximity of the actual and reference indicators’ growth rates ratios:
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
CPS | cyber-physical system |
IT | information technology |
ICT | information and communications technologies |
SME | small and midsize enterprises |
References
- Industry 4.0: What Prevents Russian Enterprises from Introducing New Technologies. Available online: https://news.rambler.ru/other/41215460-industriya-4-0-chto-meshaet-rossiyskim-predpriyatiyam-vnedryat-novye-tehnologii/ (accessed on 23 April 2021).
- Smart Factories_Industry 4.0. Available online: https://consot.ru/post_smart-factory/ (accessed on 15 May 2021).
- The Digital Economy and Society Index (DESI) 2020. Available online: https://ec.europa.eu/digital-single-market/en/digital-economy-and-society-index-desi (accessed on 2 June 2021).
- Khaitan, S.K.; McCalley, J.D. Design Techniques and Applications of Cyber-Physical Systems: A Survey. IEEE Syst. J. 2014, 9, 350–365. [Google Scholar] [CrossRef]
- Lyovin, B.A.; Tsvetkov, V.Y. Cybernetics and Physical Systems for Transport Management. World Transp. Transp. 2018, 16, 138–145. [Google Scholar]
- Balashova, E.; Palkina, E.; Schislyaeva, E. Methodological Aspects of Strategic Controlling of Digital Transformation of Transport and Logistics System. Atlantis Highlights Comput. Sci. 2019, 1, 80–85. [Google Scholar] [CrossRef] [Green Version]
- Tarasov, I.V.; Popov, N.A. Industry 4.0: Transformation of Production Factories. Strateg. Decis. Risk Manag. 2018, 3, 38–53. [Google Scholar] [CrossRef]
- Aguilar, L.A. The Need for Greater Focus on the Cybersecurity Challenges Facing Small and Midsize Businesses. Cyber Security Review; 2015; SEC.gov. Available online: https://www.sec.gov/news/statement/cybersecurity-challenges-for-small-midsize-businesses.html (accessed on 17 June 2021).
- Tsvetkov, V.Y. Control with the Use of Cyber-Physical Systems. Perspect. Sci. Educ. 2017, 3, 55–60. Available online: https://cyberleninka.ru/article/n/upravlenie-s-primeneniem-kiber-fizicheskih-sistem/viewer (accessed on 24 May 2021).
- Gurjanov, A.V.; Zakoldaev, D.A.; Shukalov, A.V.; Zharinov, I.O.; Kostishin, M.O. Industry 4.0 Digital Production Organization Based on Cyber and Physical Systems and Ontologies. Sci. Tech. J. Inf. Technol. Mech. Opt. 2018, 18, 268–276. [Google Scholar] [CrossRef] [Green Version]
- Zegzhda, P.D. Approaches to Estimation of the Security of Cyber-Physical Systems. Ruskripto 2017. Ruscrypto.ru. Available online: https://www.ruscrypto.ru/resource/archive/rc2017/files/09_zagzhda.pdf (accessed on 15 June 2021).
- Konoshenko, N. Production management. Digit. Prod. 2017, 4, 19–21. [Google Scholar]
- Colombo, A.; Bangemann, T. Industrial Cloud-Based Cyber-Physical Systems: The IMC-AESOP Approach; Springer International Publishing: Cham, Switzerland, 2014; p. 245. [Google Scholar]
- Cyber-Physical Systems in the Modern World. Toshiba Company Blog. Habr.com. Available online: https://habr.com/ru/company/toshibarus/blog/438262/ (accessed on 29 May 2021).
- Lind, M.; Watson, R.; Bergmann, M.; Ward, R.; Bjørn-Andersen, N.; Jensen, T.; Haraldson, S.; Zerem, A.; Rosemann, M. Digitizing the Maritime Eco-System-Improving Door-to-Door Coordination via a Digitized Transport Chain. STM 2018. Available online: https://fathom.world/wp-content/uploads/2018/05/STM-concept-note-11.pdf (accessed on 18 June 2021).
- Ma, S. Digital Disruption and the Future of Maritime Transport. In Economics of Maritime Business; Ma, S., Ed.; Taylor & Francis: Abingdon, UK, 2020. [Google Scholar] [CrossRef]
- Kripak, M.N.; Palkina, E.S.; Seliverstov, Y.A. Analytical Support for Effective Functioning of Intelligent Manufacturing and Transport Systems. IOP Conf. Ser. Mater. Sci. Eng. 2019, 709, 033065. [Google Scholar] [CrossRef] [Green Version]
- Rabbouch, H.; Saâdaoui, F.; Rafaa, M. Traffic Sensing and Assessing in Digital Transportation Systems. In Linking and Mining Heterogeneous and Multi-View Data; Springer International Publishing: Cham, Switzerland, 2019. [Google Scholar]
- Rad, C.-R.; Hancu, O.; Takacs, I.; Olteanu, G. Smart Monitoring of Potato Crop: A Cyber-Physical System Architecture Model in the Field of Precision Agriculture. Agric. Agric. Sci. Procedia 2015, 6, 73–79. [Google Scholar] [CrossRef] [Green Version]
- Freeman, R.E. Strategic Management: A Stakeholder Approach, 1st ed.; Harpercollins College Div: Boston, MA, USA, 1984; p. 275. [Google Scholar]
- Zaychenko, I.M.; Kozlov, A.V.; Shitova, E.S. Drivers of digital transformation of a business: Meaning, classification, key stakeholders. SPbPU J. Econ. 2020, 5, 38–49. [Google Scholar] [CrossRef]
- Schwab, K. The Fourth Industrial Revolution; Crown Business: New York, NY, USA, 2017; p. 192. [Google Scholar]
- Yastreb, N.A. Industry 4.0: Cyber-Physical Systems, Intelligent Environment, Internet of Things. Available online: https://techno.vogu35.ru/docs/2015/Industria_4_0_Yastreb.pdf (accessed on 10 June 2021).
- Rio de Janeiro Declaration on Environment and Development 1992. Available online: https://www.un.org/ru/documents/decl_conv/declarations/riodecl.shtml (accessed on 15 June 2021).
- Beutell, N.J. Values-Based Management Theory. In Encyclopedia of Business and Professional Ethics; Poff, D., Michalos, A., Eds.; Springer International Publishing: Cham, Switzerland, 2018; Available online: https://link.springer.com/referenceworkentry/10.1007/978-3-319-23514-1_74-1 (accessed on 17 June 2021).
- Business Process. Management and Modeling in BPM (Business Process Management). Available online: https://analytics.infozone.pro/bpm-business-process-management/ (accessed on 20 June 2021).
- Palkina, E.S. Using business process improvement concept to optimize enterprise production system in conditions of innovative economic development. In Proceedings of the International Conference on Modern Trends in Manufacturing Technologies and Equipment (ICMTMTE 2018), Sevastopol, Russia, 10–14 September 2018; Volume 224. MATEC Web of Conferences. Available online: https://www.matec-conferences.org/articles/matecconf/pdf/2018/83/matecconf_icmtmte2018_02011.pdf (accessed on 18 June 2021).
- Lee, E.A. The Past, Present and Future of Cyber-Physical Systems: A Focus on Models. Sensors 2015, 15, 4837–4869. [Google Scholar] [CrossRef]
- Hwang, G.; Lee, J.; Park, J.; Chang, T.-W. Developing Performance Measurement System for Internet of Things and Smart Factory Environment. Int. J. Prod. Res. 2017, 55, 2590–2602. [Google Scholar] [CrossRef]
- Wang, L.; Haghighi, A. Combined Strength of Holons, Agents and Function Blocks in Cyber-Physical Systems. J. Manuf. Syst. 2016, 40, 25–34. [Google Scholar] [CrossRef]
- Tsvetkov, V.Y. Cyber-Physical Systems. Int. J. Appl. Fundam. Res. 2017, 6, 64–65. [Google Scholar]
- Zaborovsky, V.; Lukashin, A.; Muliukha, V. Cyber-physical Object Management Platform. Osp.ru 2014, 9. Available online: https://www.osp.ru/os/2014/09/13043846 (accessed on 25 May 2021).
- Kupriyanovsky, V.P.; Namnot, E.D.; Sinyakov, S.A. Cyber-Physical Systems as the Basis of the Digital Economy. Int. J. Open Inf. Technol. 2016, 4, 18–25. [Google Scholar]
- Palkina, E.S.; Zhuravleva, N.A.; Panychev, A.Y. New Approach to Transportation Service Pricing Based on the Stakeholder Model of Corporate Governance. Mediterr. J. Soc. Sci. 2015, 6, 299–308. [Google Scholar] [CrossRef] [Green Version]
- Kluczek, A.; Żegleń, P.; Matušíková, D. The Use of Prospect Theory for Energy Sustainable Industry 4.0. Energies 2021, 14, 7694. [Google Scholar] [CrossRef]
- Rehman, A.; Saba, T.; Haseeb, K.; Larabi Marie-Sainte, S.; Lloret, J. Energy-Efficient IoT e-Health Using Artificial Intelligence Model with Homomorphic Secret Sharing. Energies 2021, 14, 6414. [Google Scholar] [CrossRef]
- Zhilenkov, A.; Chernyi, S.; Emelianov, V. Application of Artificial Intelligence Technologies to Assess the Quality of Structures. Energies 2021, 14, 8040. [Google Scholar] [CrossRef]
- Zhilenkov, A.; Chernyi, S.; Firsov, A. Autonomous Underwater Robot Fuzzy Motion Control System with Parametric Uncertainties. Designs 2021, 5, 24. [Google Scholar] [CrossRef]
- Ivanovsky, N.; Chernyi, S.; Sokolov, S.; Zhilenkov, A.; Zinchenko, A. Algorithm design for ship’s steering with specified limitations under various weather conditions. Brodogradnja 2021, 72, 19–32. [Google Scholar] [CrossRef]
- Senarak, C. Cybersecurity knowledge and skills for port facility security officers of international seaports: Perspectives of IT and security personnel. Asian J. Shipp. Logist. 2021, 37, 345–360. [Google Scholar] [CrossRef]
Main Issues of CPS Sustainability | Key Indicators of CPS Sustainability |
---|---|
Continuity of management processes in conditions of destabilizing influences |
|
Constancy of internal state through coordination of its components in order to maintain dynamic equilibrium |
|
Restoring the lost balance |
|
Overcoming the effects of external and internal environmental factors |
|
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Schislyaeva, E.; Balashova, E.; Krasovskaya, I.; Saychenko, O.; Palkina, E. Integrated Estimation of a Cyber-Physical System’s Sustainability. Energies 2022, 15, 563. https://doi.org/10.3390/en15020563
Schislyaeva E, Balashova E, Krasovskaya I, Saychenko O, Palkina E. Integrated Estimation of a Cyber-Physical System’s Sustainability. Energies. 2022; 15(2):563. https://doi.org/10.3390/en15020563
Chicago/Turabian StyleSchislyaeva, Elena, Elena Balashova, Inna Krasovskaya, Olga Saychenko, and Elena Palkina. 2022. "Integrated Estimation of a Cyber-Physical System’s Sustainability" Energies 15, no. 2: 563. https://doi.org/10.3390/en15020563
APA StyleSchislyaeva, E., Balashova, E., Krasovskaya, I., Saychenko, O., & Palkina, E. (2022). Integrated Estimation of a Cyber-Physical System’s Sustainability. Energies, 15(2), 563. https://doi.org/10.3390/en15020563