Intelligent data collaboration in heterogeneous-device IoT platforms

Sun, D; Wu, J; Yang, J; Wu, H

Intelligent data collaboration in heterogeneous-device IoT platforms

Sun, D Wu, J Yang, J Wu, H

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Journal Article
Citation:: ACM Transactions on Sensor Networks, 2021, 17, (3), pp. 1-17
Issue Date:: 2021-08-01

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Field	Value	Language
dc.contributor.author	Sun, D
dc.contributor.author	Wu, J
dc.contributor.author	Yang, J
dc.contributor.author	Wu, H
dc.date.accessioned	2022-06-14T01:31:01Z
dc.date.available	2022-06-14T01:31:01Z
dc.date.issued	2021-08-01
dc.identifier.citation	ACM Transactions on Sensor Networks, 2021, 17, (3), pp. 1-17
dc.identifier.issn	1550-4859
dc.identifier.issn	1550-4867
dc.identifier.uri	http://hdl.handle.net/10453/158148
dc.description.abstract	The merging boundaries between edge computing and deep learning are forging a new blueprint for the Internet of Things (IoT). However, the low-quality of data in many IoT platforms, especially those composed of heterogeneous devices, is hindering the development of high-quality applications for those platforms. The solution presented in this article is intelligent data collaboration, i.e., the concept of deep learning providing IoT with the ability to adaptively collaborate to accomplish a task. Here, we outline the concept of intelligent data collaboration in detail and present a mathematical model in general form. To demonstrate one possible case where intelligent data collaboration would be useful, we prepared an implementation called adaptive data cleaning (ADC), designed to filter noisy data out of temperature readings in an IoT base station network. ADC primarily consists of a denoising autoencoder LSTM for predictions and a four-level data processing mechanism to perform the filtering. Comparisons between ADC and a maximum slop method show ADC with the lowest false error and the best filtering rates.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	ACM Transactions on Sensor Networks
dc.relation.isbasedon	10.1145/3427912
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	Intelligent data collaboration in heterogeneous-device IoT platforms
dc.type	Journal Article
utslib.citation.volume	17
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-06-14T01:30:50Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	17
utslib.citation.issue	3

Abstract:

The merging boundaries between edge computing and deep learning are forging a new blueprint for the Internet of Things (IoT). However, the low-quality of data in many IoT platforms, especially those composed of heterogeneous devices, is hindering the development of high-quality applications for those platforms. The solution presented in this article is intelligent data collaboration, i.e., the concept of deep learning providing IoT with the ability to adaptively collaborate to accomplish a task. Here, we outline the concept of intelligent data collaboration in detail and present a mathematical model in general form. To demonstrate one possible case where intelligent data collaboration would be useful, we prepared an implementation called adaptive data cleaning (ADC), designed to filter noisy data out of temperature readings in an IoT base station network. ADC primarily consists of a denoising autoencoder LSTM for predictions and a four-level data processing mechanism to perform the filtering. Comparisons between ADC and a maximum slop method show ADC with the lowest false error and the best filtering rates.

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http://hdl.handle.net/10453/158148