arXiv：cs.AI· Prince Kwabena Appiah Boadu, Jeffery Baafi Kyei, Enoch Kofi Koranteng, Griffth Selorm Klogo·· 17 小时前
基于 AWS IoT Greengrass v2 的混合云边架构：面向工业预测性维护的远边缘 ML 推理部署
Seamless Deployment of Machine Learning Inference at the Far Edge: An Empirical AWS IoT Greengrass Architecture for Industrial Predictive Maintenance
AI 导读
一篇获 AiBThings 2026 接受的论文提出基于 AWS IoT Core 与 AWS IoT Greengrass v2 的混合云边架构，用于制造业预测性维护。
正文
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 and resilience. 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. 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). We measure end-to-end inference latency, bandwidth consumption and inference accuracy. Configuration B cuts cloud-bound bandwidth by 86.8% (63.6 KB to 7.2 KB per 200-reading window) and decision latency by 256x (23.1 ms cloud round trip to 0.09 ms local inference). At the edge, with no cloud involvement, it keeps an anomaly-class F1 score of 0.84 and 91% recall. The evaluation also instruments system uptime and Stream Manager queuing under cloud disconnection; the quantitative disconnection experiment is left as planned work. 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.
| Comments: | Conference paper draft in IEEE format, with code appendices. Already accepted in AiBThings - IEEE 4th International Conference on Artificial Intelligence, Blockchain and Internet of Things |
| Subjects: | Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI) |
| ACM classes: | C.2.4; I.2.6 |
| Cite as: | arXiv:2610.14815 [cs.DC] |
| (or arXiv:2610.14815v1 [cs.DC] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14815 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Prince Kwabena Appiah Boadu [view email]
[v1]
Sat, 10 Oct 2026 11:22:44 UTC (5,501 KB)
来源：arXiv：cs.AI · arxiv.org