A crowd-based image learning framework using edge computing for smart city applications
Description
Smart city applications covering a wide area such as traffic monitoring and pothole detection are gradually adopting more image machine learning algorithms utilizing ubiquitous camera sensors. To support such applications, an edge computing paradigm focuses on processing large amount of multimedia data at the edge to offload processing cost and reduce long-distance traffic and latency. However, existing edge computing approaches rely on pre-trained static models and are limited in supporting diverse classes of edge devices as well as learning models to support them. This research proposes a novel crowd-based learning framework which allows edge devices with diverse resource capabilities to perform machine learning towards the realization of image-based smart city applications. The intelligent retraining algorithm allows sharing key visual features to achieve a higher accuracy based on the temporal and geospatial uniqueness. Our evaluation shows the trade-off between accuracy and the resource constraints of the edge devices, while the model re-sizing option enables running machine learning models on edge devices with high flexibility.
Impact and interest:
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ID Code: | 209258 | ||
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Item Type: | Chapter in Book, Report or Conference volume (Conference contribution) | ||
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Additional Information: | Funding Information: ACKNOWLEDGMENT This research has been supported in part by NSF grant CNS-1461963, the USC Integrated Media Systems Center, the Annenberg Foundation, and unrestricted cash gifts from Google, Oracle and the USC Viterbi Center for Cyber-Physical Systems and the Internet of Things (CCI). | ||
Measurements or Duration: | 10 pages | ||
Keywords: | Crowd-based Learning, Crowdsourcing, Edge Devices, Machine Learning, Smart Cities | ||
DOI: | 10.1109/BigMM.2019.00-47 | ||
ISBN: | 9781728155289 | ||
Pure ID: | 76720588 | ||
Funding Information: | ACKNOWLEDGMENT This research has been supported in part by NSF grant CNS-1461963, the USC Integrated Media Systems Center, the Annenberg Foundation, and unrestricted cash gifts from Google, Oracle and the USC Viterbi Center for Cyber-Physical Systems and the Internet of Things (CCI). | ||
Copyright Owner: | Consult author(s) regarding copyright matters | ||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||
Deposited On: | 29 Mar 2021 00:20 | ||
Last Modified: | 02 Mar 2024 03:05 |
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