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 article, information diffusion within CPSS networks is studied. Information dynamic 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.

Cyber-physical systems (CPS) are highly integrated hardware-software devices that electro-mechanical components are tightly coupled with advanced computational algorithms for data collection, processing, communication, and control. Internet of Things is the emerging application of CPS. The main research challenge in designing CPS devices and systems is the quantification of complex system behaviors, such as consciousness, adaptation, and evolution. Particularly trust becomes an important element that affects system behavior in the networked society. To capture the unique human societal and systems aspects of trustworthiness quantification for CPS, in this paper, trustworthiness is measured by the perceptions of ability, benevolence, and integrity quantitatively. Ability measures one's sensing and reasoning capability and influence to others. Benevolence captures the genuineness of intention and the extent of reciprocity in information exchange. Integrity provides the confidence about system dependability and predictability. A generic probabilistic graph model is developed to represent CPS functionality at mesoscale and demonstrate the perception-based quantification of ability and benevolence. Trust-based CPS network design and optimization are also demonstrated with the metrics of ability and benevolence.