In-Network Processing Acoustic Data for Anomaly Detection in Smart Factory
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Beitragende
Abstract
Modern manufacturing is now deeply integrating new technologies such as 5G, Internet-of-things (IoT), and cloud/edge computing to shape manufacturing to a new level - Smart Factory. Autonomic anomaly detection (e.g., malfunctioning machines and hazard situations) in a factory hall is on the list and expects to be realized with massive IoT sensor deployments. In this paper, we consider acoustic data-based anomaly detection, which is widely used in factories because sound information reflects richer internal states while videos cannot; besides, the capital investment of an audio system is more economically friendly. However, a unique challenge of using audio data is that sounds are mixed when collecting thus source data separation is inevitable. A traditional way transfers audio data all to a centralized point for separation. Nevertheless, such a centralized manner (i.e., data transferring and then analyzing) may delay prompt reactions to critical anomalies. We demonstrate that this job can be transformed into an in-network processing scheme and thus further accelerated. Specifically, we propose a progressive processing scheme where data separation jobs are distributed as microservices on intermediate nodes in parallel with data forwarding. Therefore, collected audio data can be separated 43.75% faster with even less total computing resources. This solution is comprehensively evaluated with numerical simulations, compared with benchmark solutions, and results justify its advantages.
Details
Originalsprache | Englisch |
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Titel | 2021 IEEE Global Communications Conference (GLOBECOM) |
Herausgeber (Verlag) | IEEE |
Seiten | 1-6 |
ISBN (elektronisch) | 978-1-7281-8104-2 |
ISBN (Print) | 978-1-7281-8105-9 |
Publikationsstatus | Veröffentlicht - 2021 |
Peer-Review-Status | Ja |
Publikationsreihe
Reihe | IEEE Conference on Global Communications (GLOBECOM) |
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ISSN | 1930-529X |
Konferenz
Titel | 2021 IEEE Global Communications Conference, GLOBECOM 2021 |
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Dauer | 7 - 11 Dezember 2021 |
Stadt | Madrid |
Land | Spanien |
Externe IDs
Scopus | 85184630486 |
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ORCID | /0000-0001-8469-9573/work/161891010 |
Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- Anomaly Detection, In-network Computing, IoT, Smart Factory