A Next-Generation Layered CIoT Framework: Redefining ITS Architecture with Intelligent Reasoning and Adaptive Communication
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This study proposes a next-generation Cognitive Internet of Things (CIoT) framework specifically designed for Intelligent Transportation Systems (ITS). As smart cities and autonomous vehicles become increasingly prevalent, traditional ITS architectures often fail to provide the adaptability and real-time intelligence needed for complex traffic environments. To overcome these limitations, a novel multi-layered CIoT model is introduced, incorporating cognitive reasoning and edge intelligence to enhance situational awareness and vehicular coordination. The study conducts a comparative analysis between the traditional ISO/OSI 7-layer model and the proposed CIoT framework, focusing on the cognitive, network, data, and application layers. Simulations under varying traffic conditions evaluate communication latency, and t-tests validate the statistical significance of the results. The framework’s practical value is further demonstrated through ITS use cases such as adaptive traffic light control, emergency vehicle prioritization, and integrated public transport systems. Findings show that the CIoT-based ITS reduces average latency by over 30% in high-density traffic, enabling real-time hazard detection, intelligent rerouting, and seamless vehicle-to- infrastructure communication. The research highlights CIoT’s transformative potential in intelligent mobility and suggests future work on standardization and 5G integration.
Abstract
This study proposes a next-generation Cognitive Internet of Things (CIoT) framework specifically designed for Intelligent Transportation Systems (ITS). As smart cities and autonomous vehicles become increasingly prevalent, traditional ITS architectures often fail to provide the adaptability and real-time intelligence needed for complex traffic environments. To overcome these limitations, a novel multi-layered CIoT model is introduced, incorporating cognitive reasoning and edge intelligence to enhance situational awareness and vehicular coordination. The study conducts a comparative analysis between the traditional ISO/OSI 7-layer model and the proposed CIoT framework, focusing on the cognitive, network, data, and application layers. Simulations under varying traffic conditions evaluate communication latency, and t-tests validate the statistical significance of the results. The framework’s practical value is further demonstrated through ITS use cases such as adaptive traffic light control, emergency vehicle prioritization, and integrated public transport systems. Findings show that the CIoT-based ITS reduces average latency by over 30% in high-density traffic, enabling real-time hazard detection, intelligent rerouting, and seamless vehicle-to- infrastructure communication. The research highlights CIoT’s transformative potential in intelligent mobility and suggests future work on standardization and 5G integration.