An Integrated Cloud-Edge Computing Framework for Industrial IoT Applications
AUTHORS
Mikkel Sorensen,Department of Electrical Engineering, Technical University of Denmark (DTU), Lyngby, Denmark
Jensen Clara,Department of Electrical Engineering, Technical University of Denmark (DTU), Lyngby, Denmark
Emil Rasmussen,Department of Electrical Engineering, Technical University of Denmark (DTU), Lyngby, Denmark
ABSTRACT
The Industrial Internet of Things (IIoT) is revolutionizing manufacturing and production by enabling data-driven monitoring, predictive maintenance, and operational optimization. However, traditional cloud-centric architectures often face challenges with latency, bandwidth, and responsiveness when processing large-scale, real-time data from distributed industrial systems. This study presents an integrated cloud–edge computing framework that strategically distributes computational tasks between edge nodes and cloud servers to overcome these limitations. The proposed framework executes time-sensitive operations—such as anomaly detection and process control—at the edge, while delegating complex analytics, trend evaluation, and model retraining to the cloud. An adaptive middleware dynamically manages workload allocation and ensures interoperability across heterogeneous devices, enabling scalable, resilient IIoT operations. The framework was evaluated in a simulated smart factory environment using real-time sensor data streams. Results indicate that the proposed architecture significantly reduces latency, lowers network load, and improves fault-detection accuracy compared with conventional cloud-only and edge-only approaches. Furthermore, the system exhibits high scalability and robustness across diverse industrial scenarios. These findings demonstrate the potential of cloud–edge integration to enable adaptive, efficient, and sustainable IIoT ecosystems, offering practical insights for advancing Industry 4.0 implementations.
KEYWORDS
Cloud-edge computing, Industrial Internet of Things (IIoT), Real-time data processing, Predictive maintenance, Smart manufacturing, Industry 4.0
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