Distributed Computational Architectures for Intelligent Edge–Cloud IoT Integration

This study presents a distributed computational architecture that integrates edge and cloud computing paradigms to enable intelligent IoT operations. The proposed framework distributes computational tasks between edge devices and cloud platforms according to workload characteristics, resource availability, and application requirements. Advanced data processing, machine learning-based decision-making, and adaptive resource allocation mechanisms are incorporated to improve system responsiveness and operational efficiency. The architecture supports real-time analytics, enhanced fault tolerance, reduced communication overhead, and optimized energy consumption. Experimental evaluation demonstrates significant improvements in processing latency, throughput, and scalability compared with conventional centralized approaches. The findings highlight the potential of distributed edge–cloud architectures to support next-generation smart environments, including industrial automation, healthcare monitoring, smart cities, and autonomous systems.

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