Seamless Deployment of Machine Learning Inference at the Far Edge: An Empirical AWS IoT Greengrass Architecture for Industrial Predictive Maintenance

Key points

• Industrial IoT sensors produce continuous, high-volume data streams that predictive maintenance needs to analyse in real time.1
• We deploy two configurations on real AWS infrastructure and compare them: a cloud-only baseline (Configuration A) and the proposed hybrid edge architecture (Configuration B).1
• We measure end-to-end inference latency, bandwidth consumption and inference accuracy.1
• Our review of the literature points to four gaps that this work addresses: the lack of empirically validated Greengrass v2 deployments for predictive maintenance, unquantified bandwidth savings from selective edge synchronization, unmeasured resilience under cloud disconnection, and the open MLOps feedback loop at the edge.1

Sources (1)

• [1]Seamless Deployment of Machine Learning Inference at the Far Edge: An Empirical AWS IoT Greengrass Architecture for Industrial Predictive Maintenance
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 11:22 AM

  This paper presents a hybrid cloud-edge architecture for manufacturing predictive maintenance built on AWS IoT Core and AWS IoT Greengrass v2, with local machine learning inference at the edge and selective cloud synchronization.

  Industrial IoT sensors produce continuous, high-volume data streams that predictive maintenance needs to analyse in real time.

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