Intelligent Sensor-Driven Integration Framework for IoT-Enabled Public Transportation Using an Extended CAMS Architecture

The rapid proliferation of heterogeneous IoT sensor networks in urban public transporta-tion systems generates large volumes of real-time data that are often fragmented across in-dependent platforms, thereby limiting interoperability, scalability, and coordinated intel-ligence. Existing architectures typically treat sensing, edge processing, and artificial intel-ligence as loosely coupled components, lacking unified frameworks that support real-time adaptive decision-making in complex transportation environments. To address this gap, this study proposes a sensor-centric extension of the CAMS architec-ture that integrates semantic sensor interoperability, edge-enabled distributed processing, and embedded AI-driven coordination within a unified framework. The sensor-centric ex-tended CAMS framework introduces a distributed sensor integration layer combined with a native intelligent coordination module that enables real-time multi-sensor fusion and predictive analytics. A functional prototype is evaluated using hybrid real-world and simulated datasets representing vehicle telemetry, infrastructure sensing, and passenger demand across diverse operational scenarios. Experimental results demonstrate signifi-cant improvements in interoperability efficiency, predictive accuracy, scalability, and end-to-end latency compared with conventional centralized architectures. The results indicate that tightly integrating distributed sensing with embedded intelli-gence enhances robustness and scalability in smart transportation ecosystems. The pro-posed architecture provides a practical and extensible foundation for next-generation in-telligent urban mobility systems and advances the integration of IoT sensing and AI-driven decision-making in large-scale cyber–physical environments.