Demonstration of in-network audio processing for low-latency anomaly detection in smart factories
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed
Contributors
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
Original language | English |
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Title of host publication | 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC) |
Pages | 933-934 |
Number of pages | 2 |
Publication status | Published - 11 Jan 2022 |
Peer-reviewed | No |
Publication series
Series | IEEE Consumer Communications and Networking Conference |
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ISSN | 2331-9852 |
External 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 |
Keywords
ASJC Scopus subject areas
Keywords
- Internet of Things, audio processing, in-network computing, network softwarization