Situational models of information processing in Internet of Things systems

The rapid growth of connected objects in IoT systems has led to a growing demand for intelli-gent systems for automated and simplified management of everyday life. These systems aim to simplify the management of functions such as lighting, heating, security, and appliance opera-tion, contributing to comfort and energy efficiency. From this perspective, situational context, which encompasses information about the physical environment, user preferences, current ac-tivities, and other relevant variables, can significantly enhance the ability of IoT systems to forecast and respond to the needs of residents. However, integrating situational context into IoT systems presents significant technical and conceptual challenges. One of the key challenges is the development of accurate and dynamic situational models that can take into account diverse and changing parameters. In addition, the need to process large amounts of data from multiple IoT devices in real time requires a reliable and scalable architecture. In the field of the Internet of Things, where new technologies transform our living environment into dynamic and adaptive ecosystems, the ability of situational modeling and inference becomes an important support for ensuring intelligent and personalized interaction between users and their environment. The agent-oriented approach is a natural way to build various natural and artificial systems that dif-fer in their respective complexity, dynamism, situation, and autonomy. In particular, a strong conceptual connection exists between agents and intelligent IoT components, as well as between multi-agent systems and IoT. Thus, taking into account the entire set of requirements and prob-lems associated with the development of Internet of Things systems, the agent-oriented ap-proach can be used to model, program, and simulate Internet of Things applications and systems and systematically promote and accelerate their development. The aspects of situational model-ing and inference considered in the article allow for forming a technological basis for intelli-gent support of the processes of functioning in Internet of Things systems.