Demonstration of in-network audio processing for low-latency anomaly detection in smart factories
Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/Gutachten › Beitrag in Konferenzband › Beigetragen
Beitragende
Abstract
This demonstration focuses on in-network computing as an enabler for low-latency Industrial Internet of Things (IIoT) applications, such as audio source separation for anomaly detection. By demonstrating a specific industrial application, we show that our method Progressive ICA (pICA), improves accuracy and reduces overall service latency progressively. The idea is to parallelize data transmission and processing along a multi-hop path consisting of in-network computing nodes. The audience can experience the benefits of the novel concept of in-network computing by interacting with the demonstration remotely via the Internet or in person.
Details
Originalsprache | Englisch |
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Titel | 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC) |
Seiten | 933-934 |
Seitenumfang | 2 |
Publikationsstatus | Veröffentlicht - 11 Jan. 2022 |
Peer-Review-Status | Nein |
Publikationsreihe
Reihe | IEEE Consumer Communications and Networking Conference |
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ISSN | 2331-9852 |
Externe IDs
Scopus | 85135730895 |
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dblp | conf/ccnc/WuST0F22 |
ORCID | /0000-0001-7008-1537/work/142248628 |
ORCID | /0000-0001-8469-9573/work/161890992 |
Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- Internet of Things, audio processing, in-network computing, network softwarization