Analysis of Time Series Data Generated from the Internet of Things Using Deep Learning Models

Polycarp Shizawaliyi Yakoi; Xiangfu Meng; Shuolin Cui; Danladi Suleman; Xueyong Yang

doi:10.1109/ACCESS.2023.3331762

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Analysis of Time Series Data Generated from the Internet of Things Using Deep Learning Models

Journal article

Open access

Peer reviewed

Analysis of Time Series Data Generated from the Internet of Things Using Deep Learning Models

Polycarp Shizawaliyi Yakoi, Xiangfu Meng, Shuolin Cui, Danladi Suleman and Xueyong Yang

IEEE Access, Vol.11, pp.133313-133328

2023

DOI: https://doi.org/10.1109/ACCESS.2023.3331762

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Abstract

Internet of Things (IoT)

deep learning

IoT data analysis

time series

The ever-increasing widespread use of the Internet of Things and its applications has generated massive amounts of data. IoT sensor-generated datasets typically have a time-series structure and relational metadata to describe them. Time series data are typically data that have timestamps and can be obtained from sensors and IoT devices. Data prediction is required to maximize the potential of IoT-generated data, and anomaly detection and correction are needed to preserve data quality and integrity. Traditional machine learning models are incapable of analyzing the gigantic amounts of IoT-generated data. On the other hand, deep learning can properly analyze large data volumes, leading to increased use in the IoT domain. This research has examined the use of deep learning models for prediction, anomaly detection, and correction of data generated by IoT devices. The study found that deep learning is widely used in different fields today to analyze IoT-generated data. The research also outlines some challenges being faced while using deep learning models for IoT data analysis. More research is suggested in this study to expose more challenges and tackle the current challenges to achieve better IoT data analysis using deep learning.

Details

Title: Analysis of Time Series Data Generated from the Internet of Things Using Deep Learning Models
Authors: Polycarp Shizawaliyi Yakoi (Author) - Liaoning Technical University
Xiangfu Meng (Author) - Liaoning Technical University
Shuolin Cui (Author) - Liaoning Technical University
Danladi Suleman (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Xueyong Yang (Author) - Liaoning Technical University
Publication details: IEEE Access, Vol.11, pp.133313-133328
Publisher: Institute of Electrical and Electronics Engineers
Date published: 2023
DOI: 10.1109/ACCESS.2023.3331762
ISSN: 2169-3536
Organisation Unit: School of Science, Technology and Engineering
Language: English
Record Identifier: 99978198502621
Output Type: Journal article

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