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AWS Greengrass v2 enables efficient edge ML for industrial predictive maintenance

By PulseAugur Editorial · [1 sources] ·

This paper details a hybrid cloud-edge architecture for industrial predictive maintenance using AWS IoT Greengrass v2. The proposed system significantly reduces cloud-bound bandwidth by 86.8% and decision latency by 256x compared to a cloud-only approach. The edge deployment maintains a strong F1 score of 0.84 for anomaly detection and 91% recall, even without cloud connectivity. AI

IMPACT This architecture could significantly reduce operational costs and improve real-time decision-making for industrial AI applications.

RANK_REASON This is a research paper detailing a specific architecture and its empirical evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AWS Greengrass v2 enables efficient edge ML for industrial predictive maintenance

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19 / 100
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This is a research paper detailing a specific architecture and its empirical evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Prince Kwabena Appiah Boadu, Jeffery Baafi Kyei, Enoch Kofi Koranteng, Griffth Selorm Klogo ·

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

    arXiv:2610.14815v1 Announce Type: cross Abstract: Industrial IoT sensors produce continuous, high-volume data streams that predictive maintenance needs to analyse in real time. Cloud-centric architectures send all raw telemetry to central servers, which costs latency, bandwidth a…

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