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Topology-informed information dynamics modeling in cyber–physical–social system networks

Published online by Cambridge University Press:  14 July 2021

Yan Wang*
Affiliation:
Georgia Institute of Technology, Atlanta, GA, USA
*
Author for correspondence: Yan Wang, E-mail: yan-wang@gatech.edu

Abstract

Cyber–physical–social systems (CPSS) are physical devices that are embedded in human society and possess highly integrated functionalities of sensing, computing, communication, and control. CPSS rely on their intense collaboration and information sharing through networks to be functioning. In this paper, topology-informed network information dynamics models are proposed to characterize the evolution of information processing capabilities of CPSS nodes in networks. The models are based on a mesoscale probabilistic graph model, where the sensing and computing capabilities of the nodes are captured as the probabilities of correct predictions. A topology-informed vector autoregression model and a latent variable vector autoregression model are proposed to model the correlations between prediction capabilities of nodes as linear functional relationships. A hybrid Gaussian process regression model is also developed to capture both the nonlinear spatial and temporal correlations between nodes. The new information dynamics models are demonstrated and tested with a simulator of CPSS networks. The results show that the topological information of networks can improve the efficiency in constructing the time series models. The network topology also has influences on the prediction capabilities of CPSS.

Type
Research Article
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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References

Bańbura, M and Modugno, M (2014) Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data. Journal of Applied Econometrics 29, 133160.CrossRefGoogle Scholar
Batini, C and Scannapieco, M (2016) Data and Information Quality: Dimensions, Principles and Techniques. Switzerland: Springer. doi:10.1007/978-3-319-24106-7CrossRefGoogle Scholar
Burmester, M, Magkos, E and Chrissikopoulos, V (2012) Modeling security in cyber–physical systems. International Journal of Critical Infrastructure Protection 5, 118126.CrossRefGoogle Scholar
Du, N, Song, L, Woo, H and Zha, H (2013) Uncover topic-sensitive information diffusion networks. Proceedings of the 16th International Conference on Artificial Intelligence and Statistics, Scottsdale, AZ, USA, pp. 229–237.Google Scholar
Gruhl, D, Guha, R, Liben-Nowell, D and Tomkins, A (2004) Information diffusion through blogspace. Proceedings of the 13th International Conference on World Wide Web, New York, New York, USA, pp. 491–501.CrossRefGoogle Scholar
Horváth, I (2019) A computational framework for procedural abduction done by smart cyber-physical systems. Designs 3, 1.CrossRefGoogle Scholar
Horváth, I and Gerritsen, BH (2012) Cyber-physical systems: Concepts, technologies and implementation principles. Proceedings of the 9th International Symposium on Tools and Methods of Competitive Engineering (TMCE2012), May 7–11, Karlsruhe, Germany, pp. 19–36.Google Scholar
Horváth, I and Wang, J (2015) Towards a comprehensive theory of multi-aspect interaction with cyber physical systems. Proceedings of ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE2015), August 2–5, Boston, Massachusetts, USA, pp. V01BT02A009.CrossRefGoogle Scholar
Iwata, T, Shah, A and Ghahramani, Z (2013) Discovering latent influence in online social activities via shared cascade Poisson processes. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 11–14, Chicago, Illinois, USA, pp. 266–274.CrossRefGoogle Scholar
Jeon, J., Chun, I., & Kim, W. (2012). Metamodel-based CPSS modeling tool. In Park J, Jeong YS, Park S, Chen HC (eds) Embedded and Multimedia Computing Technology and Service, Lecture Notes in Electrical Engineering, Vol. 181. Dordrecht: Springer, pp. 285291.Google Scholar
Kleineberg, KK and Boguná, M (2014) Evolution of the digital society reveals balance between viral and mass media influence. Physical Review X 4, 031046.CrossRefGoogle Scholar
Lee, KH, Hong, JH and Kim, TG (2015 a) System of systems approach to formal modeling of CPSS for simulation-based analysis. ETRI Journal 37, 175185.CrossRefGoogle Scholar
Lee, EA, Niknami, M, Nouidui, TS and Wetter, M (2015 b) Modeling and simulating cyber-physical systems using CyPhySim. Proceedings of the 12th IEEE International Conference on Embedded Software, Oct. 4–9, Amsterdam, Netherlands, pp. 115–124.CrossRefGoogle Scholar
Li, Y, Horváth, I and Rusák, Z (2018) Constructing personalized messages for informing cyber-physical systems based on dynamic context information processing. Proceedings of the 12th International Symposium on Tools and Methods of Competitive Engineering (TMCE2018), May 7–11, Las Palmas de Gran Canaria, Spain. pp. 105–120.Google Scholar
Lu, H, Lv, S, Jiao, X, Wang, X and Liu, J (2015) Maximizing information diffusion in the cyber-physical integrated network. Sensors 15, 2851328530.CrossRefGoogle ScholarPubMed
Magureanu, G, Gavrilescu, M, Pescaru, D and Doboli, A (2010) Towards UML modeling of cyber-physical systems: a case study for gas distribution. Proceedings of IEEE 8th International Symposium on Intelligent Systems and Informatics, Sept. 10–11, Subotica, Serbia, pp. 471–476.CrossRefGoogle Scholar
Newman, ME (2002) Spread of epidemic disease on networks. Physical Review E 66, 016128.CrossRefGoogle ScholarPubMed
Palachi, E, Cohen, C and Takashi, S (2013) Simulation of cyber physical models using SysML and numerical solvers. Proceedings of 2013 IEEE International Systems Conference (SysCon), April 15–18, Orlando, FL, USA, pp. 671–675.CrossRefGoogle Scholar
Petnga, L and Austin, M (2016) An ontological framework for knowledge modeling and decision support in cyber-physical systems. Advanced Engineering Informatics 30, 7794.CrossRefGoogle Scholar
Pourtalebi, S and Horváth, I (2017) Information schema constructs for instantiation and composition of system manifestation features. Frontiers of Information Technology & Electronic Engineering 18, 13961415.CrossRefGoogle Scholar
Saeedloei, N and Gupta, G (2011) A logic-based modeling and verification of CPSS. ACM SIGBED Review 8, 3134.CrossRefGoogle Scholar
Simma, A and Jordan, MI (2010) Modeling events with cascades of poisson processes. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, Catalina Island, CA, USA, pp. 546–555.Google Scholar
Tavčar, J and Horváth, I (2018) A review of the principles of designing smart cyber-physical systems for run-time adaptation: learned lessons and open issues. IEEE Transactions on Systems, Man, and Cybernetics: systems 49, 145158.CrossRefGoogle Scholar
Vroom, RW and Horváth, I (2014) Cyber-physical augmentation: an exploration. Proceedings of the 10th International Symposium on Tools and Methods of Competitive Engineering (TMCE2014), May 19–23, Budapest, Hungary, pp. 1509–1520.Google Scholar
Wang, Y (2016) System resilience quantification for probabilistic design of Internet-of-Things architecture. Proceedings of 2016 ASME International Design Engineering Technical Conferences & The Computer and Information in Engineering Conference (IDETC/CIE2016), August 21–24, Charlotte, North Carolina, USA, pp. V01BT02A011.CrossRefGoogle Scholar
Wang, Y (2018 a) Resilience quantification for probabilistic design of cyber-physical system networks. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering 4, 031006.CrossRefGoogle Scholar
Wang, Y (2018 b) Trustworthiness in designing cyber-physical systems. Proceedings of 12th International Symposium on Tools and Methods of Competitive Engineering (TMCE2018), May 7–11, Las Palmas de Gran Canaria, Spain, pp. 27–40.Google Scholar
Wang, Y. (2018 c). Trust based cyber-physical systems network design. Proceedings of ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE2018), August 26–29, Quebec City, Quebec, Canada, pp. V01AT02A037.Google Scholar
Wang, Y (2018 d) Trust quantification for networked cyber-physical systems. IEEE Internet of Things Journal 5, 20552070.CrossRefGoogle Scholar
Wang, Y (2020 a) Information dynamics in the network of cyber-physical systems. Proceedings of the 2020 International Symposium on Tools & Methods of Competitive Engineering (TMCE2020), May 11–15, 2020, Dublin, Ireland, pp. 13–26.Google Scholar
Wang, Y (2020 b) Quantifying trust perception to enable design for connectivity in cyber-physical-social systems. In Fukuda, S (ed), Emotional Engineering, Vol. 8: Emotion in the Emerging World. Cham, Switzerland: Springer, pp. 85114.CrossRefGoogle Scholar
Wang, Y (2021 a) Probabilistic modeling of information dynamics in networked cyber-physical-social systems. IEEE Internet of Things Journal (in press). https://doi.org/10.1109/JIOT.2021.3072893Google Scholar
Wang, Y (2021 b) Design of trustworthy cyber-physical-social systems with discrete Bayesian optimization. Journal of Mechanical Design 143, 071702.CrossRefGoogle Scholar
Wang, J, Jiang, C, Han, Z, Quek, TQ and Ren, Y (2017) Private information diffusion control in cyber physical systems: a game theory perspective. Proceedings of 2017 IEEE 26th International Conference on Computer Communication and Networks (ICCCN), July 31–August 3, Vancouver, BC, Canada, pp. 1–10.CrossRefGoogle Scholar
Wu, F, Huberman, BA, Adamic, LA and Tyler, JR (2004) Information flow in social groups. Physica A: Statistical Mechanics and its Applications 337, 327335.CrossRefGoogle Scholar
Yagan, O, Qian, D, Zhang, J and Cochran, D (2013) Conjoining speeds up information diffusion in overlaying social-physical networks. IEEE Journal on Selected Areas in Communications 31, 10381048.CrossRefGoogle Scholar
Yang, J and Leskovec, J (2010) Modeling information diffusion in implicit networks. 2010 IEEE International Conference on Data Mining, Dec. 13–17, Sydney, NSW, Australia, pp. 599–608.CrossRefGoogle Scholar
Yi, Y, Zhang, Z and Gan, C (2019) The outbreak threshold of information diffusion over social–physical networks. Physica A: Statistical Mechanics and its Applications 526, 121128.CrossRefGoogle Scholar

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