More Like this

1. Design of Trustworthy Cyber–Physical–Social Systems With Discrete Bayesian Optimization

   https://doi.org/10.1115/1.4049532  

   Wang, Yan (July 2021, Journal of Mechanical Design)

   null (Ed.)

   Abstract 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 the trustworthiness of CPSS by quantitative metrics of ability, benevolence, and integrity. The CPSS network architecture can be optimized by choosing a subnet such that the trust metrics are maximized. The combinatorial network optimization problem, however, is computationally challenging. Most of the available global optimization algorithms for solving such problems are heuristic methods. In this paper, a surrogate-based discrete Bayesian optimization method is developed to perform network design, where the most trustworthy CPSS network with respect to a reference node is formed to collaborate and share information with. The applications of ability and benevolence metrics in design optimization of CPSS architecture are demonstrated. 

   more » « less
2. Trust Based Cyber-Physical Systems Network Design

   Wang, Yan (August 2018, ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Cyber-physical systems (CPS) extensively share information with each other, work collaboratively over Internet of Things, and seamlessly integrated with human society. Designing CPS requires the new consideration of design for connectivity where security, privacy, and trust are of the main concerns. Particularly trust can affect system behavior in a networked environment. In this paper, trustworthiness is quantitatively measured by the perceptions of ability, benevolence, and integrity. Ability indicates the capabilities of sensing, reasoning, and influence in a society. Benevolence measures the genuineness of intention and reciprocity in information exchange. Integrity captures the system predictability and dependability. With these criteria, trust-based CPS network design and optimization are demonstrated. 

   more » « less
3. Trustworthiness in designing cyber-physical systems

   Wang, Yan (May 2018, Proceedings of 12th International Symposium on Tools and Methods of Competitive Engineering (TMCE2018))

   Cyber-physical systems (CPS) provide unique functions of data collection, processing, communication, and control. The advanced capabilities and functions of CPS rely on their highly networked working environment and deep interdependency. The effectiveness of their performance critically depends on what and how they share among each other. Designing a trustworthy network that CPS can work together collaboratively thus is important. In order to design trustable CPS products, quantitative measures of trustworthiness are required. In this paper, quantitative metrics of trustworthiness, including capability, benevolence, and integrity, are proposed based on a new probabilistic graph model. The proposed metrics can be calculated from either subjective perception or objective information of network topology. A design optimization framework based on the trustworthiness metrics is also demonstrated. 

   more » « less
4. Topology-informed information dynamics modeling in cyber–physical–social system networks

   https://doi.org/10.1017/S0890060421000159  

   Wang, Yan (July 2021, Artificial Intelligence for Engineering Design, Analysis and Manufacturing)

   null (Ed.)

   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. 

   more » « less
5. 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. 

   more » « less