Hybrid Cloud-Edge Infrastructures for Scalable IIoT AI Deployments
TL;DR
This paper explores hybrid cloud-edge infrastructures as a scalable solution for deploying AI in IIoT environments and presents an architectural framework that balances compute-intensive model training in the cloud with low-latency inference at the edge.
Abstract
Industrial Internet of Things (IIoT) ecosystems are increasingly reliant on artificial intelligence (AI) to enable predictive analytics, real-time control, and intelligent automation. However, the centralized nature of traditional cloud computing introduces latency, bandwidth, and privacy constraints that limit the real-time applicability of AI models in industrial settings. This paper explores hybrid cloud-edge infrastructures as a scalable solution for deploying AI in IIoT environments. We present an architectural framework that balances compute-intensive model training in the cloud with low-latency inference at the edge. Key challenges including model orchestration, data management, and security in distributed environments are analyzed. Through case studies and performance evaluations, we demonstrate how hybrid architectures can effectively support scalable and resilient AI deployments for a range of industrial applications. Our findings highlight open research challenges and provide recommendations for building robust hybrid IIoT systems.