The DOI matches its Crossref record; 174 of the 174 references it cites were found in a registry or an index.
Data quality challenges in large-scale cyber-physical systems: A systematic review
Open accessAbstract
Cyber-physical systems (CPSs) are integrated systems engineered to combine computational control algorithms and physical components such as sensors and actuators, effectively using an embedded communication core. Smart cities can be viewed as large-scale, heterogeneous CPSs that utilise technologies like the Internet of Things (IoT), surveillance, social media, and others to make informed decisions and drive the innovations of automation in urban areas. Such systems incorporate multiple layers and complex structure of hardware, software, analytical algorithms, business knowledge and communication networks, and operate under noisy and dynamic conditions. Thus, large-scale CPSs are vulnerable to enormous technical and operational challenges that may compromise the quality of data of their applications and accordingly reduce the quality of their services. This paper presents a systematic literature review to investigate data quality challenges in smart-cities large-scale CPSs and to identify the most common techniques used to address these challenges. This systematic literature review showed that significant work had been conducted to address data quality management challenges in smart cities, large-scale CPS applications. However, still, more is required to provide a practical, comprehensive data quality management solution to detect errors in sensor nodes’ measurements associated with the main data quality dimensions of accuracy, timeliness, completeness, and consistency. No systematic or generic approach was demonstrated for detecting sensor nodes and sensor node networks failures in large-scale CPS applications. Moreover, further research is required to address the challenges of ensuring the quality of the spatial and temporal contextual attributes of sensor nodes’ observations.
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In the citing paper
“The scope of the SLR conducted by Alwan et al. [58] is limited to the data quality challenges and approaches for smart cities.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [58]
In the citing paper
“Data mining methods (i.e., anomaly detection, classification, clustering, and predictive analysis) are the most widely used compared to others.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [58]
In the citing paper
“The scope of the SLR is limited to the data quality challenges for CPSs, the data quality techniques to overcome these challenges, and the effectiveness of these techniques.The SLR does not cover data quality metrics, data security, or software engineering solutions to manage data quality.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [58]
In the citing paper
“Alwan et al. revealed that data quality issues occur in large-scale CPSs because of sensor malfunctions, calibration issues, poor sensor node quality, environmental effects, external noise, and networks or communication errors.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [58]
In the citing paper
“More recently, Alwan et al. [58] conducted another SLR to investigate data quality challenges in smart cities as large-scale CPSs and identify the most common techniques used to address these challenges.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [58]
In the citing paper
“This is because the traditional data validation methods are insufficient for handling the dynamic and heterogeneous nature of Internet of Things (IoT) data streams.”
From Semantic-Driven Approach for Validation of IoT Streaming Data in Trustable Smart City Decision-Making and Monitoring Systems (2025) · cites this paper as [5]
In the citing paper
“Meanwhile, current approaches to improve the accuracy of sensor readings majorly involve the use of statistical or probabilistic models [5] and point calibrations [6].”
From Semantic-Driven Approach for Validation of IoT Streaming Data in Trustable Smart City Decision-Making and Monitoring Systems (2025) · cites this paper as [5]
In the citing paper
“A probabilistic model such as Machine Learning can extract meaningful insights from streams but will require initial data storage for model training/learning before such a model can be applied.”
From Semantic-Driven Approach for Validation of IoT Streaming Data in Trustable Smart City Decision-Making and Monitoring Systems (2025) · cites this paper as [5]
Citations
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Cited by
Sorted by citations
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
Data Quality Management in Big Data: Strategies, Tools, and Educational Implications
Nguyen, T., Nguyen, H.-T., & Nguyen-Hoang, T.-A. (2025) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2025.105067 · Cited by 20
Enabling privacy-aware interoperable and quality IoT data sharing with context
Chhetri, T. R., Dehury, C. K., Varghese, B., Fensel, A., Srirama, S. N., & DeLong, R. J. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.03.039 · Cited by 16
Bamgboye, O., Liu, X., Cruickshank, P., & Liu, Q. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9040108 · Cited by 4
“Meanwhile, current approaches to improve the accuracy of sensor readings majorly involve the use of statistical or probabilistic models [5] and point calibrations [6].”
Metadata
- Authors
- Alwan, A. A., Ciupala, M. A., Brimicombe, A. J., Ghorashi, S. A., Baravalle, A., & Falcarin, P.
- Affiliations
- University of East London (GB)
- Akamai (United States) (US)
- Corresponding author
- Falcarin, Paolo
- ORCID
- Venue
- Information Systems
- Publisher
- Elsevier BV
- Published
- 2022-03
- Volume
- 105
- Pages
- 101951
- ISSN
- 0306-4379 (ISSN-L)
- OpenAlex
- W3215300303
- Cited by
- 68
- References
- 174
- Citations by year
- 2026: 12 · 2025: 19 · 2024: 16 · 2023: 12 · 2022: 9
- FWCI
- 1.86
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Information Systems (publishedVersion)
- ARCA (Università Ca' Foscari Venezia) (submittedVersion, other-oa)
- Funders
- University of East London
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Quality and Management
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Social Sciences › Urban Studies › Smart Cities and Technologies
- Keywords
- data quality, cyber-physical systems, smart cities, Internet of Things, sensor fault detection, data quality dimensions, accuracy, timeliness, completeness, consistency, spatio-temporal context, large-scale systems
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- The paper
- Alwan, A. A., Ciupala, M. A., Brimicombe, A. J., Ghorashi, S. A., Baravalle, A., & Falcarin, P. (2022). Data quality challenges in large-scale cyber-physical systems: A systematic review. Information Systems. https://doi.org/10.1016/j.is.2021.101951
