Modeling of the Process of Sensory Data Dissemination in the Information-Centric Network ICN

The relevance The Information-Centric Network (ICN) is a promising concept for describing and modeling modern information and telecommunications. In an ICN, information interaction is considered conditionally independent of the telecommunications technologies used, and it is carried out on the basis of named data objects. In Industry 4.0 digital systems, including the Internet of Things, rapid situational awareness across a network of interacting objects requires disseminating essential information. It is necessary to objectively analyze the overall dynamics of sensor information distribution in the form of up-to-date data during information processing at ICN nodes. The current status of the data enables effective decision-making regarding the management of objects and information flows. To increase speed, sensor data is stored in the semantic cache memory of ICN network nodes. This paper presents an approach to studying the distribution of up-to-date data in ICNs based on information diffusion using deterministic models based on epidemic simulation. Obtaining data on the current status of a significant event from the source node is analogous to the conditional infection of ICN nodes. This approach enables us to estimate the rate at which ICN nodes update information from the current source and make management decisions based on the exchange of operational knowledge in the form of sensor information. The aim of this study: improving the efficiency of information flow control in the ICN network for the propagation of sensor data. Methods: analytical review of scientific publications, numerical methods, simulation modeling. Solution: An analytical model of data propagation with topical status in the ICN network was developed based on a system of nonlinear ordinary differential equations that describe the diffusion processes of the SIR model. The results of the independent discrete-event modeling of the propagation of ICN sensor data in the ns-3 simulator are presented. The modeling algorithm is based on applying a deterministic epidemic model. Scientific novelty lies in the authors' proposed approach to analyzing and displaying the dynamics of content distribution in an ICN network during a significant event, as well as estimating the maximum distribution rate of topical data. The practical significance consists of assessing the intensity of changes in situational awareness in sensory networks when topical information is disseminated.