Cloud-Edge AI Integration for Real-Time Data Processing in Industrial Internet of Things (IIoT)
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I3P102Keywords:
Industrial Internet of Things (IIoT), Cloud Computing, Edge Computing, Artificial Intelligence (AI), Real-time Data Processing, Predictive Maintenance, Federated Learning, Model CompressionAbstract
The Industrial Internet of Things (IIoT) promises a revolution in manufacturing and industrial processes through ubiquitous sensing, data collection, and intelligent automation. However, the sheer volume and velocity of data generated by IIoT devices pose significant challenges for traditional cloud-centric architectures. This paper explores the integration of Cloud and Edge AI for real-time data processing in IIoT environments. We discuss the limitations of solely cloud-based solutions and highlight the advantages of leveraging edge computing to perform local data processing and inference. The paper proposes a hybrid architecture that distributes AI tasks between the cloud and edge, enabling real-time responses, reduced latency, improved bandwidth utilization, and enhanced data security. We delve into specific algorithms and techniques suitable for edge-based AI inference, including model compression, quantization, and federated learning. Furthermore, we present a case study demonstrating the practical implementation of Cloud-Edge AI integration for predictive maintenance in a smart manufacturing setting. The findings demonstrate the efficacy of the proposed architecture in enabling faster decision-making, improved operational efficiency, and reduced downtime in IIoT applications. Finally, the paper concludes with a discussion of future research directions and potential applications of Cloud-Edge AI in the evolving landscape of the IIoT
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