Data Quality Management in the Internet of Things
Open accessAbstract
Nowadays, IoT is being used in more and more application areas and the importance of IoT data quality is widely recognized by practitioners and researchers. The requirements for data and its quality vary from application to application or organization in different contexts. Many methodologies and frameworks include techniques for defining, assessing, and improving data quality. However, due to the diversity of requirements, it can be a challenge to choose the appropriate technique for the IoT system. This paper surveys data quality frameworks and methodologies for IoT data, and related international standards, comparing them in terms of data types, data quality definitions, dimensions and metrics, and the choice of assessment dimensions. The survey is intended to help narrow down the possible choices of IoT data quality management technique.
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Zhang et al. and Liu et al. study literature on data quality based on quality issues, dimensions, and measures but exclude data quality techniques and their solution domain.
“For instance, Zhang et al. [156] and Liu et al. [110] study the literature on data quality based on quality issues, dimensions, and measures but exclude the data quality techniques (e.g., data repair) and their solution domain (i.e., the abstract environment where the data quality technique is developed).”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [156]
The paper does review data-quality issues, dimensions, and metrics (Section 4). But its Abstract says it surveys data-quality frameworks and methodologies to help choose management techniques, and Section 3 reviews those techniques and screens frameworks by their applicability to the IoT domain. That contradicts the claim that Zhang et al. exclude techniques and their solution domain; this verdict does not assess Liu et al. (LLM, conf=0.88)
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“Furthermore, when selecting interpolation techniques, it is important to consider the accuracy of the interpolation value [63], which is expected to meet user requirements.”
BackgroundA Risk Evaluation Framework in System Control Subject to Sensor Degradation and Failure
Yuan, T., Xu, W., Adjallah, K. H., Wang, H., Liu, L., & Xu, J. (2024) · Sensors · doi:10.3390/s24051550 · Cited by 6 · see citing works
“Furthermore, there is a distinction in data quality between the development and application phases of such algorithms [6].”
BackgroundEvaluating Data Quality: Comparative Insights on Standards, Methodologies, and Modern Software Tools
Alexakis, T., Adamopoulou, E. F., Πεππές, Ν., Daskalakis, E., & Ntouskas, G. (2025) · Electronics · doi:10.3390/electronics14153038 · Cited by 8 · see citing works
Kitkowska, A., Brodén, K., & Abdullah, L. (2024) · IEEE Access · doi:10.1109/access.2024.3469558 · Cited by 6 · see citing works
“In short, the experts provided ideas about either full-functioning systems (mainly considering functional requirements, e.g., systems to use in the cafeteria and engage students in better dietary behaviors) or solutions to individual problems (mainly in non-functional requirements, e.g.,…”
BackgroundSmart City IoT Data Management with Proactive Middleware
Kolekar, V. K., Oswal, M., Wankhade, Y., Shirke, G., Sondur, A., & Wable, P. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i5s.6754 · Cited by 6 · see citing works
Construction 4.0 technologies for advancing quality management processes
Ogunrinde, O., Gartoumi, K. I., Osuizugbo, I. C., Hatamleh, M. T., & Oyeyipo, O. O. (2025) · Engineering Construction & Architectural Management · doi:10.1108/ecam-01-2025-0007 · Cited by 5 · see citing works
Towards an IoT-Enabled Digital Earth for SDGs: The Data Quality Challenge
Syed, M. S. B., Kelly, P., Stacey, P., & Berry, D. (2024) · IEEE Access · doi:10.1109/access.2024.3478813 · Cited by 5 · see citing works
Challenges in Data Quality Management for IoT-Enhanced Event Logs
Bertrand, Y., Schultheis, A., Malburg, L., Grüger, J., Asensio, E. S., & Bergmann, R. (2025) · Lecture Notes in Business Information Processing · doi:10.1007/978-3-031-92474-3_2 · Cited by 4 · see citing works
Quality matters: A decadal systematic exploration of data quality in IoT environment
Kaur, T., & Kaur, P. D. (2025) · Data & Knowledge Engineering · doi:10.1016/j.datak.2025.102533 · Cited by 4 · see citing works
Nugraha, T. F., Wibowo, W. S., Genia, V., Fadhil, A., & Ruldeviyani, Y. (2024) · Journal of Information Systems Engineering and Business Intelligence · doi:10.20473/jisebi.10.1.51-69 · Cited by 10 · see citing works
Current Challenges of Big Data Quality Management in Big Data Governance: A Literature Review
Bena, Y. A., Ibrahim, R. B., & Mahmood, J. (2024) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-59711-4_15 · Cited by 4 · see citing works
IoT Based Models in Healthy Natural Resource Management: Healthy Soils for Healthy Food Productions
Kumar, D., Shanthakumar, S., Banerjee, M., & Hanspal, M. S. (2024) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-74374-0_11 · Cited by 4 · see citing works
Kochanek, A., Angrecka, S., Pietrucha, I., Zacłona, T., Petryk, A., Generowicz, A., Akbulut, L., & Atılgan, A. (2025) · Sustainability · doi:10.3390/su18010385 · Cited by 6 · see citing works
Leveraging measurement data quality by adoption of the FAIR guiding principles
Schmitt, R. H., Bodenbenner, M., Hamann, T., Sanders, M. P., Moser, M., & Abdelrazeq, A. (2024) · tm - Technisches Messen · doi:10.1515/teme-2024-0040 · Cited by 3 · see citing works
Paasche, S., & Groppe, S. (2023) · Future Internet · doi:10.3390/fi15110349 · Cited by 3 · see citing works
Application of Internet of Things (IoT) in Biomedicine: Challenges and Future Directions
Fuior, R., Sălceanu, A., Luca, C. M., & Corciovă, C. (2023) · IntechOpen eBooks · doi:10.5772/intechopen.113178 · Cited by 3 · see citing works
A Hybrid Simulation Platform for quality-aware evaluation of complex events in an IoT environment
Gkoulis, D., Bardaki, C., Νικολαϊδου, Μ., Kousiouris, G., & Tsadimas, A. (2024) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2024.102919 · Cited by 3 · see citing works
Towards Quality Management Procedures in 3D Cadastre
Višnjevac, N., Šoškić, M., & Mihajlović, R. (2024) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi13050160 · Cited by 3 · see citing works
“Data quality indicators are defined from different perspectives [20,26–28].”
Indexing in WoT to Locate Indoor Things
Faheem, M. R., Anees, T., Hussain, M., Ditta, A., Alquhayz, H., & Khan, M. A. (2023) · IEEE Access · doi:10.1109/access.2023.3272691 · Cited by 3 · see citing works
“According to the author [17], IoT data might be symbolic, continuous, quantitative, discrete, static, or streaming in nature.”
BackgroundHafid, A., Sebtaoui, F. E., & Mouchtachi, A. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-16122-5 · Cited by 2 · see citing works
“Automated quality inspection, utilizing IoT-enabled vision systems, automates illness detection, ensuring real-time identity of first-class troubles for the duration of production and minimizing errors [65, 66].”
The Truth About Labels: Unveiling The Hidden Risk to Supervised Machine Learning Models
Dix, M., Manca, G., & Fay, A. (2025) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa65518.2025.11205690 · Cited by 2 · see citing works
“The negative impact of data quality issues on ML model performance is widely recognized, particularly in industrial contexts [9]–[12].”
Al-Okdeh, S. K., Al-Sulaiti, I., Shwedeh, F., Alzoubi, H. M., & Ahmed, G. (2026) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83608-578-220251023 · Cited by 2 · see citing works
Internet of Things - New Insights [Working Title]
Habib, M. K. (2023) · IntechOpen eBooks · doi:10.5772/intechopen.111033 · Cited by 2 · see citing works
Idrees, R., & Maiti, A. (2024) · Applied Sciences · doi:10.3390/app14135907 · Cited by 2 · see citing works
Data Quality Assessment for ML Decision-Making
Moloiu, A.-Ş., Albeanu, G., Madsen, H., & Popențiu-Vlădicescu, F. (2023) · Springer series in reliability engineering · doi:10.1007/978-3-031-21232-1_8 · Cited by 2 · see citing works
Data Quality Driven Design Patterns for Internet of Things
Rath, C. K., Mandal, A. K., & Sarkar, A. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-6791-7_18 · Cited by 2 · see citing works
Syed, M. S. B., Kelly, P., Stacey, P., & Berry, D. (2025) · Data in Brief · doi:10.1016/j.dib.2025.112071 · Cited by 2 · see citing works
“Validity refers to the ‘degree to which the data accurately represents or corresponds to the real world it is intended to measure’ [ 44 ].”
Schultheis, A., Bertrand, Y., Grüger, J., Malburg, L., Bergmann, R., & Serral Asensio, E. (2025) · IoT · doi:10.3390/iot6040063 · Cited by 1 · see citing works
“For a recent, in-depth discussion of these approaches, we redirect the reader to Zhang et al. [55].”
InfluentialData Management in a Smart Campus Project: An Ontological Approach
Lima, R., Santos, H., & Paiva, S. (2023) · International Conference on Engineering Management of Communication and Technology (EMCTECH) · doi:10.1109/emctech58502.2023.10297017 · Cited by 1 · see citing works
Xie, W., Yin, J., & Liu, Y. (2024) · IEEE International Conference on Frontiers Technology of Information and Computer (ICFTIC) · doi:10.1109/icftic64248.2024.10913286 · Cited by 1 · see citing works
Ensuring Data Quality and Integrity in Modern Enterprises
Vayyala, R. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-0365-9.ch002 · Cited by 1 · see citing works
Construction of data governance indicator system for manufacturing enterprises based on EFA and CFA
Zhuo, X., & Zhang, L. (2025) · Journal of Computational Methods in Sciences and Engineering · doi:10.1177/14727978251362024 · Cited by 1 · see citing works
Self-citationInan, M. S. K., Liao, K., Shen, H., Jayaraman, P. P., Montori, F., & Georgakopoulos, D. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3595556 · Cited by 1 · see citing works
“To mitigate this, it is essential to perform rigorous QA on IoT datasets by adhering to established IoT data quality frameworks [42], [43], with a particular focus on metadata.”
Reflexive Thematic Analysis of Big Data Governance Practices in Public Higher Learning Institutions
Bena, Y. A., Ibrahim, R. B., Mahmood, J., Alhussian, H., Abdulkadir, S. J., Nasser, M., & Ayemowa, M. O. (2025) · IEEE Access · doi:10.1109/access.2025.3610092 · Cited by 1 · see citing works
Daten, Datenmanagement und Datenauswertung
Leimeister, J. M. (2025) · Springer eBooks · doi:10.1007/978-3-662-70646-6_3 · Cited by 1 · see citing works
FiQoT : An Energy‐Efficient Fog‐Assisted Architecture for Quality‐Driven IoT ‐Enabled Systems
Kaur, T., & Kaur, P. D. (2025) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70338 · Cited by 1 · see citing works
“There are a few data quality dimensions mentioned in [17, 42, 43].”
Toward Consistent Data Quality Assessment in Heterogeneous Smart Living Systems
Dreesbach, T., Dankert, L., Brîncoveanu, C., Carl, K. V., Hinz, O. F., & Thomas, O. (2025) · IEEE Conference on Business Informatics · doi:10.1109/cbi68102.2025.00027 · Cited by 1 · see citing works
“When evaluating data quality, appropriate dimensions should be selected based on the specific data quality challenges [24].”
InfluentialSemantic Core for Sensor Telemetry Ingestion for Digital Twins
Osolinskyi, O., Lipianina-Honcharenko, K., & Komar, M. (2026) · Smart Cities · doi:10.3390/smartcities9050077 · Cited by 1 · see citing works
Gkoulis, D., Tsadimas, A., Kousiouris, G., Bardaki, C., Dimitrakopoulos, G. J., Anagnostopoulos, D., & Νικολαϊδου, Μ. (2026) · Internet of Things · doi:10.1016/j.iot.2026.102051 · Cited by 1 · see citing works
Utilizing Knowledge Bases in Filtering IoT Data to Predict Algal Blooms
Manik, L. P., Puspasari, R., Riyanto, S., Albasri, H., Triharyuni, S., Yaman, A., & Wibowo, S. (2023) · International Conference on Computer, Control, Informatics and its Applications · doi:10.1109/ic3ina60834.2023.10285783 · Cited by 1 · see citing works
“Missing data, also known as incomplete data, is the second most frequent defect in sensor data [5].”
BackgroundHybrid Deep Learning Modelfor Enhancing the Streaming Efficiency of 6G Enabled Massive IoT Systems
Kaliaperumal, K., Lakshmisridevi, S., Shargunam, S., Gomathy, V., Pathak, P., & Manojkumar, B. (2024) · Wireless Personal Communications · doi:10.1007/s11277-024-11249-2 · Cited by 1 · see citing works
Data Quality Assessment: A Practical Application
Silva, E. C. E., Peixoto, T., Oliveira, Ó., & Oliveira, B. (2025) · Lecture notes in mechanical engineering · doi:10.1007/978-3-031-94484-0_42 · Cited by 1 · see citing works
IoT Data Ness: From Streaming to Added Value
Correia, R., Sousa, C., & Carneiro, D. R. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-27409-1_64
PAPA for IoT - Role of Data Accuracy in IoT Deployment and Data-centric Decision-Making
Vermanen, M., Naskali, J., Harkke, V., & Koskinen, J. S. S. (2023) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro57284.2023.10159749
“[26] Furthermore, organisation-level governance highly affects the outputs of data-centric decision-making [27], [28].”
BackgroundNizam, M. K., Goyal, S. B., Verma, C., & Illés, Z. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-2839-8_48
Data Science for Quality Management
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“…health technology data Declerck et al. [24] Health data (for secondary use) Schmidt et al. [25] Observational health research data Zhang et al. [26] Internet of Things (IoT) data Ijab et al. [27] Big data (in the public sector) Daikeler et al. [28] Digital social science research data Cichy &…”
Effective Data Quality Assurance Integrating Data Modelling and Governance
Verma, V., & Deshmukh, S. (2025) · International Conference on Innovative Mechanisms for Industry Applications · doi:10.1109/icimia67127.2025.11200744
Wang, F., Li, H., & Xie, Q. (2025) · International Conference on Telecommunications and Power Electronics (TELEPE) · doi:10.1109/telepe66277.2025.00066
“Geolocation sensors track machinery placement, while real-time dashboards ensure that only authorized personnel operate the equipment, enhancing safety and efficiency [3].”
Awick, J.-P., Steffens, L., Karl, M., & Gómez, J. M. (2024) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/dss64273.2024.00013
“To facilitate international data management standardization and establish uniform DQ standards, various ISO standards have been published [20].”
Data Quality Evaluation Model for Engineering Data Transmission Links
Lan, X., Han, C., Kang, Q., Chen, X., Li, X., Zhai, S., Pan, W., Huang, K., Song, J., Li, S., et al. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-6946-5_57
Scalable Data Governance Through Engineering-Driven Quality and Consistency Control
Pamula, G. K. (2026) · IEEE International Conference on AI in Cybersecurity (ICAIC) · doi:10.1109/icaic67076.2026.11395829
Andersson, E. (2026) · Informatics · doi:10.3390/informatics13080136
“This scope allows for an in-depth examination of some of the most common and relevant dimensions [10,47].”
Idrees, R., & Amin, M. B. (2026) · Cluster Computing · doi:10.1007/s10586-026-06416-y
ARAS: Round-Level Availability for Unattended Antarctic LoRa Monitoring
Yang, Z., Ding, H., Hu, Q., & Tao, T. (2026) · IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia) · doi:10.1109/icpsasia70813.2026.11692069
“' Research on data quality and information quality provides the theoretical basis. accuracy, completeness, consistency, and timeliness [15] .”
Rahman, M. A., Reza, M. N., Zakir, E., Kang, J.-Y., Rashid, M. M., Robin, S., & Chung, S. (2026) · Agronomy · doi:10.3390/agronomy16202046
- Authors
- Zhang, L., Jeong, D., & Lee, S.
- Affiliations
- Kunsan National University (KR)
- Corresponding author
- Lee, Sukhoon
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-08-30
- Volume
- 21
- Issue
- 17
- Pages
- 5834
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC8434542
- OpenAlex
- W3197620075
- Cited by
- 77
- References
- 78
- Citations by year
- 2026: 12 · 2025: 26 · 2024: 17 · 2023: 19 · 2022: 3
- FWCI
- 1.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Research Foundation of Korea (2019R1I1A3A01060826)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- Internet of Things, data quality, data quality management, data quality framework, data quality metrics, data quality dimensions, international standards, IoT data
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