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A Dependable Time Series Analytic Framework for Cyber-Physical Systems of IoT-based Smart Grid
Open accessAbstract
With the emergence of cyber-physical systems (CPS), we are now at the brink of next computing revolution. The Smart Grid (SG) built on top of IoT (Internet of Things) is one of the foundations of this CPS revolution, which involves a large number of smart objects connected by networks. The volume of time series of SG equipment is tremendous and the raw time series are very likely to contain missing values because of undependable network transferring. The problem of storing a tremendous volume of raw time series thereby providing a solid support for precise time series analytics now becomes tricky. In this article, we propose a dependable time series analytics (DTSA) framework for IoT-based SG. Our proposed DTSA framework is capable of providing a dependable data transforming from CPS to the target database with an extraction engine to preliminary refining raw data and further cleansing the data with a correction engine built on top of a sensor-network-regularization-based matrix factorization method. The experimental results reveal that our proposed DTSA framework is capable of effectively increasing the dependability of raw time series transforming between CPS and the target database system through the online lightweight extraction engine and the offline correction engine. Our proposed DTSA framework would be useful for other industrial big data practices.
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Dependable raw time series gathered from multiple sensors are very likely to contain missing values, which could harm the accuracy of data analytics.
“Wang et al. [44] state that dependable, raw time series (time series gathered from multiple sensors) are very likely to contain missing values, which could harm the accuracy of data analytics.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [44]
Wang et al. provide only offline repair.
“They provide only offline repair.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [44]
They evaluate data repair approaches predicting missing or corrupted data.
“They evaluate data repair approaches predicting missing or corrupted data.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [44]
Only three studies (i.e., [27,33,44]) use regression metrics to assess the performance of data quality techniques, not their impact on the performance of classification/regression models.
“As mentioned above, only three studies (i.e., [27,33,44]) use regression metrics to assess the performance of data quality techniques, not their impact on the performance of classification/regression models.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [44]
Five primary studies [1,14,26,42,44] focus on data quality management in the Cloud-based architecture layers (L4, L5, and L6).
“Five primary studies [1,14,26,42,44] focus on data quality management in the Cloudbased architecture layers (L4, L5, and L6).”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [44]
Three of these studies [14,42,44] propose data quality management techniques (data repair and cleaning) running on the Cloud.
“Three of these studies [14,42,44] propose data quality management techniques (data repair and cleaning) running on the Cloud.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [44]
These techniques are offline, e.g., data repair for large historical datasets on the Cloud.
“These techniques are offline, e.g., data repair for large historical datasets on the Cloud.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [44]
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Cited by
Sorted by citations
Nikooghadam, M., Amintoosi, H., Islam, S. H., & Moghadam, M. F. (2021) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2020.101955 · Cited by 140
A Systematic Review of Data Quality in CPS and IoT for Industry 4.0
Goknil, A., Nguyen, P., Sen, S., Politaki, D., Niavis, H., Pedersen, K. J., Suyuthi, A., Anand, A., & Ziegenbein, A. (2023) · ACM Computing Surveys · doi:10.1145/3593043 · Cited by 84
Metadata
- Authors
- Wang, C., Zhu, Y., Shi, W., Chang, V., Vijayakumar, P., Liu, B., Mao, Y., Wang, J., & Fan, Y.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Xi’an Jiaotong-Liverpool University (CN)
- University College of Engineering Tindivanam, Tindivanam, Tamilnadu, India
- Shanghai Jiao Tong University, Minhang District, Shagnhai, P.R.C
- Venue
- ACM Transactions on Cyber-Physical Systems
- Published
- 2018-08-29
- Volume
- 3
- Issue
- 1
- Pages
- 1-18
- ISSN
- 2378-962X (ISSN-L)
- 2378-9638
- OpenAlex
- W2889300442
- Cited by
- 43
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 4 · 2022: 6 · 2021: 8 · 2020: 13 · 2019: 7 · 2018: 2
- FWCI
- 5.37
- Type
- journal article
- Language
- en
- Open access
- green
- research.tees.ac.uk
- Locations
- ACM Transactions on Cyber-Physical Systems (publishedVersion)
- TeesRep (Teesside University) (submittedVersion, other-oa)
- Teesside University Research Portal (Teesside University) (submittedVersion, other-oa)
- Funders
- State Grid Corporation of China
- National Natural Science Foundation of China (61373032)
- State Grid Science and Technology Project (520626140020 and 14H100000552)
- National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme
- National Key Research and Development Program of China (2016YFE0100600)
- National High Technology Research and Development Program of China 863 Program (2015AA050204)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid and Power Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Keywords
- smart grid, Internet of Things, cyber-physical systems, missing data imputation, data cleaning, data transformation, big data storage, industrial big data
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