research-article

Intelligent Data Collaboration in Heterogeneous-device IoT Platforms

Authors:

Huifeng WuAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 17, Issue 3

Article No.: 22, Pages 1 - 17

https://doi.org/10.1145/3427912

Published: 21 June 2021 Publication History

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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Intelligent Data Collaboration in Heterogeneous-device IoT Platforms

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Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 17, Issue 3

August 2021

333 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/3470624

Editor:
Yunhao Liu
Tsinghua University, China

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Copyright © 2021 Association for Computing Machinery.

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 21 June 2021

Accepted: 01 October 2020

Revised: 01 August 2020

Received: 01 April 2020

Published in TOSN Volume 17, Issue 3

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National Key R&D Program of China
Science and Technology Program of Zhejiang Province

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https://doi.org/10.1109/ACCESS.2024.3443067
Awaisi KYe QSampalli S(2024)A Survey of Industrial AIoT: Opportunities, Challenges, and DirectionsIEEE Access10.1109/ACCESS.2024.342627912(96946-96996)Online publication date: 2024
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Ullah IAdhikari DSu XPalmieri FWu CChoi C(2024)Integration of data science with the intelligent IoT (IIoT): current challenges and future perspectivesDigital Communications and Networks10.1016/j.dcan.2024.02.007Online publication date: Mar-2024
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