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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 Open the ORCID record for Yan Wang [Opens in a new window]
Yan Wang*
Affiliation:
Georgia Institute of Technology, Atlanta, GA, USA
*
Author for correspondence: Yan Wang, E-mail: [email protected]
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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.

Keywords

complex systemsGaussian process regressioninformation diffusionprobabilistic graph modelvector autoregression
Type
Research Article
Information
AI EDAM , Volume 35 , Issue 3 , August 2021 , pp. 316 - 331
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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