Intelligent-Contextual Model for the Classification of Internet of Things Environments

This paper presents an intelligent-contextual model for classifying Internet of Things (IoT) environments, based on the comprehensive consideration of key parameters such as the level of embedded intelligence, operational context, security level, and system dynamics. The relevance of the study stems from the rapid growth in the complexity of IoT ecosystems, which are characterized by heterogeneous devices, diverse operating conditions, the need for adaptability to environmental changes, and increased security requirements. A critical analysis of existing IoT classification approaches reveals their major limitations: a narrow focus on purely technical or functional features, inadequate reflection of device behavior and intelligence, and the disregard for operational context and threat landscape. The developed model addresses these shortcomings and enables more accurate structuring of IoT environments by taking into account their intended purpose, architectural characteristics, and ability to autonomously make decisions. It can be applied for system analysis, risk formalization, optimization of threat response mechanisms, and the design of effective cybersecurity strategies and adaptive control in dynamic environments. The proposed classification makes it possible to generalize existing IoT system types and lays the groundwork for developing new, more flexible and secure architectures. The practical significance of the results lies in their applicability across industries where intelligent data processing, contextual awareness, and resilience to cyberthreats are essential - including Industry 4.0, eHealth, intelligent transport systems, and smart cities.