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1. Probabilistic Modeling of Information Dynamics in Networked Cyber-Physical-Social Systems

   https://doi.org/10.1109/JIOT.2021.3072893  

   Wang, Yan (April 2021, IEEE Internet of Things Journal)

   null (Ed.)

   Cyber-physical-social systems (CPSS) are physical devices with highly integrated functions of sensing, computing, communication and control, and are seamlessly embedded in human society. The levels of intelligence and functions that CPSS can perform rely on their extensive collaboration and information sharing through networks. In this paper, information diffusion within CPSS networks is studied. Information dynamics models are proposed to characterize the evolution of information processing and decision making capabilities of individual CPSS nodes. The data-driven statistical models are based on a mesoscale probabilistic graph model, where the individual nodes' sensing and computing functions are represented as the probabilities of correct predictions, whereas the communication functions are represented as the probabilities of mutual influences between nodes. A copula dynamics model is proposed to explicitly capture the information dependency among individuals with joint prediction probabilities and estimated from extremal probabilities. A topology-informed vector autoregression model is proposed to represent the mutual influence of prediction capabilities. A spatial-temporal hybrid Gaussian process regression model is also proposed to simultaneously capture correlations between nodes and temporal correlation in the time series. 

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2. Information dynamics in the network of cyber-physical systems

   Wang, Yan (May 2020, Proceedings of 13th International Symposium on Tools and Methods of Competitive Engineering (TMCE2020))

   null (Ed.)

   Cyber-physical systems (CPS) are physical devices with highly integrated functionalities of sensing, computing, communication, and control. The levels of intelligence and functions that CPS can perform heavily rely on their intense collaboration and information sharing through networks. In this paper, the information propagation within CPS networks is studied. Information dynamics models are proposed to characterize the evolution of information processing capabilities of CPS nodes in networks. The models are based on a mesoscale probabilistic graph model, where the sensing and computing functions of CPS nodes are captured as the probabilities of correct predictions, whereas the communication functions are represented as the probabilities of mutual influences between nodes. In the proposed copula dynamics model, the information dependency among individuals is represented with joint prediction probabilities and estimated from copulas of extremal probabilities. In the proposed functional interdependency model, the correlations between prediction capabilities are captured with their functional relationships. A data-driven approach is taken to train the parameters of the information dynamics models with data from simulations. The information dynamics models are demonstrated with a simulator of CPS networks. 

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3. Mapping epileptic directional brain networks using intracranial EEG data

   https://doi.org/10.1093/biostatistics/kxz056  

   Li, Huazhang; Wang, Yaotian; Tanabe, Seiji; Sun, Yinge; Yan, Guofen; Quigg, Mark S.; Zhang, Tingting (December 2019, Biostatistics)

   Summary The human brain is a directional network system, in which brain regions are network nodes and the influence exerted by one region on another is a network edge. We refer to this directional information flow from one region to another as directional connectivity. Seizures arise from an epileptic directional network; abnormal neuronal activities start from a seizure onset zone and propagate via a network to otherwise healthy brain regions. As such, effective epilepsy diagnosis and treatment require accurate identification of directional connections among regions, i.e., mapping of epileptic patients’ brain networks. This article aims to understand the epileptic brain network using intracranial electroencephalographic data—recordings of epileptic patients’ brain activities in many regions. The most popular models for directional connectivity use ordinary differential equations (ODE). However, ODE models are sensitive to data noise and computationally costly. To address these issues, we propose a high-dimensional state-space multivariate autoregression (SSMAR) model for the brain’s directional connectivity. Different from standard multivariate autoregression and SSMAR models, the proposed SSMAR features a cluster structure, where the brain network consists of several clusters of densely connected brain regions. We develop an expectation–maximization algorithm to estimate the proposed model and use it to map the interregional networks of epileptic patients in different seizure stages. Our method reveals the evolution of brain networks during seizure development. 

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4. Design of Trustworthy Cyber-Physical-Social Systems With Discrete Bayesian Optimization

   https://doi.org/10.1115/DETC2020-22661  

   Wang, Yan (August 2020, Proceedings of ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE2020))

   null (Ed.)

   Cyber-physical-social systems (CPSS) with highly integrated functions of sensing, actuation, computation, and communication are becoming the mainstream consumer and commercial products. The performance of CPSS heavily relies on the information sharing between devices. Given the extensive data collection and sharing, security and privacy are of major concerns. Thus one major challenge of designing those CPSS is how to incorporate the perception of trust in product and systems design. Recently a trust quantification method was proposed to measure trustworthiness of CPSS by quantitative metrics of ability, benevolence, and integrity. In this paper, the applications of ability and benevolence metrics in design optimization of CPSS architecture are demonstrated. A Bayesian optimization method is developed to perform trust based CPSS network design, where the most trustworthy network with respect to a reference node can be selected to collaborate and share information with. 

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5. Identifying Bottleneck Nodes using Packet Delay Statistics

   https://doi.org/10.1109/MILCOM47813.2019.9021045  

   Marker, Joshua; Shea, John M.; Wong, Tan F.; Graves, Eric; Yu, Paul L. (November 2019, Proceedings of the IEEE Military Communications Conference (MILCOM))

   null (Ed.)

   An algorithm to identify the bottleneck nodes linking two component networks in a simple network of networks (NoN) configuration is proposed. The proposed bottleneck identification algorithm is based on applying a support vector machine on clustered packet delay measurements. This algorithm has the advantage that it requires almost no information about the topology of the underlying NoN. Simulation results show that this algorithm can provide very good detection performance when the component networks of the NoN are not too small in size, or when the connectivity between nodes within the component networks is not too sparse. 

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