IoT Data Quality Assessment Framework Using Adaptive Weighted Estimation Fusion
Open accessAbstract
Timely data quality assessment has been shown to be crucial for the development of IoT-based applications. Different IoT applications’ varying data quality requirements pose a challenge, as each application requires a unique data quality process. This creates scalability issues as the number of applications increases, and it also has financial implications, as it would require a separate data pipeline for each application. To address this challenge, this paper proposes a novel approach integrating fusion methods into end-to-end data quality assessment to cater to different applications within a single data pipeline. By using real-time and historical analytics, the study investigates the effects of each fusion method on the resulting data quality score and how this can be used to support different applications. The study results, based on two real-world datasets, indicate that Kalman fusion had a higher overall mean quality score than Adaptive weighted fusion and Naïve fusion. However, Kalman fusion also had a higher computational burden on the system. The proposed solution offers a flexible and efficient approach to addressing IoT applications’ diverse data quality needs within a single data pipeline.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The continuous increase in the number of connected IoT devices has made it hard to assess the IoT data quality with common assessment methods.
“The continuous increase in the number of connected IoT devices has made it hard to assess the IoT data quality with common assessment methods [11].”
From Semantic-Driven Approach for Validation of IoT Streaming Data in Trustable Smart City Decision-Making and Monitoring Systems (2025) · cites this paper as [11]
The Introduction projects a large number of connected devices and says existing assessment solutions can misrepresent IoT data quality by evaluating only one stage of the data cycle. It does not say that the increasing number of devices is what makes assessment with common methods difficult. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The internet of things: a survey
Li, S., Xu, L., & Zhao, S.-S. (2014) · Information Systems Frontiers · doi:10.1007/s10796-014-9492-7 · Cited by 9,888 · see citing works
AIMQ: a methodology for information quality assessment
Lee, Y. W., Strong, D. M., Kahn, B. K., & Wang, R. Y. (2002) · Information & Management · doi:10.1016/s0378-7206(02)00043-5 · Cited by 1,611 · see citing works
A Review of Data Fusion Techniques
Castanedo, F. (2013) · The Scientific World Journal · doi:10.1155/2013/704504 · Cited by 1,027 · see citing works
Multisensor fusion and integration: approaches, applications, and future research directions
Luo, R. C., Yih, C., & Su, K. L. (2002) · IEEE Sensors Journal · doi:10.1109/jsen.2002.1000251 · Cited by 621 · see citing works
Optimal Estimation of Dynamic Systems
Crassidis, J. L., & Junkins, J. L. (2011) · Chapman & Hall/CRC eBooks · doi:10.1201/b11154 · Cited by 614 · see citing works
Vito, S. D., Massera, E., Piga, M., Martinotto, L., & Francia, G. D. (2007) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2007.09.060 · Cited by 546 · see citing works
Kalman Filter and Its Application
Li, Q., Li, R., Ji, K., & Dai, W. (2015) · International Conference on Intelligent Networks and Intelligent Systems · doi:10.1109/icinis.2015.35 · Cited by 481 · see citing works
Data Quality in Internet of Things: A state-of-the-art survey
Karkouch, A., Mousannif, H., Al Moatassime, H., & Noel, T. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.08.002 · Cited by 378 · see citing works
Gevaert, C. M., Suomalainen, J. M., Tang, J., & Kooistra, L. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2406339 · Cited by 363 · see citing works
Sensor models and multisensor integration
Durrant-Whyte, H. F. (1988) · The International Journal of Robotics Research · doi:10.1177/027836498800700608 · Cited by 352 · see citing works
Improving Adaptive Kalman Estimation in GPS/INS Integration
Ding, W., Wang, J., Rizos, C., & Kinlyside, D. (2007) · Journal of Navigation · doi:10.1017/s0373463307004316 · Cited by 338 · see citing works
Data quality: A survey of data quality dimensions
Sidi, F., Panahy, P. H. S., Affendey, L. S., Jabar, M. A., Ibrahim, H., & Mustapha, A. (2012) · International Conference on Information Retrieval & Knowledge Management (CAMP) · doi:10.1109/infrkm.2012.6204995 · Cited by 229 · see citing works
Faniel, I. M., & Jacobsen, T. E. (2010) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-010-9117-8 · Cited by 199 · see citing works
Task Offloading in Fog Computing for Using Smart Ant Colony Optimization
Kishor, A., & Chakarbarty, C. (2021) · Wireless Personal Communications · doi:10.1007/s11277-021-08714-7 · Cited by 174 · see citing works
Devillers, R., Bédard, Y., Jeansoulin, R., & Moulin, B. (2006) · International Journal of Geographical Information Systems · doi:10.1080/13658810600911879 · Cited by 140 · see citing works
Real-time big data analytics: Applications and challenges
Mohamed, N., & Al‐Jaroodi, J. (2014) · International Conference on High Performance Computing & Simulation (HPCS) · doi:10.1109/hpcsim.2014.6903700 · Cited by 111 · see citing works
Random Weighting Method for Multisensor Data Fusion
Gao, S., Zhong, Y., & Li, W. (2011) · IEEE Sensors Journal · doi:10.1109/jsen.2011.2107896 · Cited by 111 · see citing works
Multi-Sensor Data Fusion in Cluster based Wireless Sensor Networks Using Fuzzy Logic Method
Manjunatha, P., Verma, A., & Srividya, A. (2008) · doi:10.1109/iciinfs.2008.4798453 · Cited by 108 · see citing works
IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction
Lai, X., Yang, T., Wang, Z., & Chen, P. (2019) · Applied Sciences · doi:10.3390/app9091831 · Cited by 100 · see citing works
Real-time range estimation in electric vehicles using fuzzy logic classifier
Çeven, S., Albayrak, A., & Bayır, R. (2020) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2020.106577 · Cited by 83 · see citing works
Dimensions of Business Processes Quality (QoBP)
Heravizadeh, M., Mendling, J., & Rosemann, M. (2009) · Lecture notes in business information processing · doi:10.1007/978-3-642-00328-8_8 · Cited by 81 · see citing works
Adaptive weighted fusion: A novel fusion approach for image classification
Xu, Y. D., & Lu, Y. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.05.070 · Cited by 77 · see citing works
Data Quality Management in the Internet of Things
Zhang, L., Jeong, D., & Lee, S. (2021) · Sensors · doi:10.3390/s21175834 · Cited by 77 · cited 1× · see citing works
“Several other solutions have also been reported [12].”
BackgroundFuzzy logic systems and medical applications
Vlamou, E., & Papadopoulos, B. (2019) · AIMS neuroscience · doi:10.3934/neuroscience.2019.4.266 · Cited by 73 · see citing works
Shrinkage and model selection with correlated variables via weighted fusion
Daye, Z. J., & Jeng, X. J. (2008) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2008.11.007 · Cited by 69 · see citing works
Escamilla-Ambrosio, P. J., & Mort, N. (2003) · doi:10.1109/icif.2002.1021000 · Cited by 54 · see citing works
Data quality assessment and analysis for pest identification in smart agriculture
Yang, J., Lan, G., Li, Y., Gong, Y., Zhang, Z., & Erċışlı, S. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.108322 · Cited by 49 · see citing works
Asymptotic properties of the least squares estimators of the parameters of the chirp signals
Nandi, S., & Kundu, D. (2004) · Annals of the Institute of Statistical Mathematics · doi:10.1007/bf02530540 · Cited by 39 · see citing works
Towards integrated Data Analysis Quality: Criteria for the application of Industrial Data Science
West, N., Gries, J., Brockmeier, C., Göbel, J. C., & Deuse, J. (2021) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri51335.2021.00024 · Cited by 39 · see citing works
Evaluating Sensor Data Quality in Internet of Things Smart Agriculture Applications
Fizza, K., Jayaraman, P. P., Banerjee, A., Georgakopoulos, D., & Ranjan, R. K. (2021) · IEEE Micro · doi:10.1109/mm.2021.3137401 · Cited by 39 · see citing works
Distributed Decomposed Data Analytics in Fog Enabled IoT Deployments
Taneja, M., Jalodia, N., & Davy, A. (2019) · IEEE Access · doi:10.1109/access.2019.2907808 · Cited by 38 · see citing works
Kuemper, D., Iggena, T., Toenjes, R., & Pulvermueller, E. (2018) · ACM Multimedia Systems Conference (MMSys) · doi:10.1145/3204949.3204972 · Cited by 37 · see citing works
Welch, G. (2020) · Springer eBooks · doi:10.1007/978-3-030-03243-2_716-1 · Cited by 36 · see citing works
Multilayered fuzzy behavior fusion for real-time reactive control of systems with multiple sensors
Goodridge, S. G., Kay, M. G., & Luo, R. C. (1996) · IEEE Transactions on Industrial Electronics · doi:10.1109/41.499811 · Cited by 35 · see citing works
Considerations when evaluating real‐world data quality in the context of fitness for purpose
Reynolds, M. W., Bourke, A., & Dreyer, N. A. (2020) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.5010 · Cited by 34 · see citing works
Hamouda, Y. E. M., & Msallam, M. M. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3386-4 · Cited by 29 · see citing works
A Framework for Weighted Fusion of Multiple Statistical Models of Shape and Appearance
Butakoff, C., & Frangi, A. F. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.215 · Cited by 28 · see citing works
Using Trust as a Measure to Derive Data Quality in Data Shared IoT Deployments
Byabazaire, J., O'Hare, G., & Delaney, D. (2020) · 2020 29th International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn49398.2020.9209633 · Cited by 28 · see citing works
Self-citationEnd-to-End Data Quality Assessment Using Trust for Data Shared IoT Deployments
Byabazaire, J., O’Hare, G. M. P., & Delaney, D. T. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3203853 · Cited by 28 · see citing works
Self-citationResearch on Data Fusion of Adaptive Weighted Multi-source Sensor
Li, D., Shen, C., Dai, X., Zhu, X., Luo, J., Li, X., Chen, H., & Liang, Z. (2019) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2019.06354 · Cited by 26 · see citing works
Weighted Data Fusion Use for Ruthenium Dioxide Thin Film pH Array Electrodes
Liao, Y.-H., & Chou, J.-C. (2009) · IEEE Sensors Journal · doi:10.1109/jsen.2009.2024045 · Cited by 22 · see citing works
Developing House of Information Quality framework for IoT systems
Alrae, R., Nasir, Q., & Talib, M. W. A. (2020) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-020-00989-6 · Cited by 22 · see citing works
Sensor Abnormal Detection and Recovery Using Machine Learning for IoT Sensing Systems
Tsai, F.-K., Chen, C.-C., Chen, T.-F., & Lin, T. (2019) · IEEE International Conference on Industrial Engineering and Applications (ICIEA) · doi:10.1109/iea.2019.8715215 · Cited by 21 · see citing works
Considerations for system design in IoT-based autonomous ecological sensing
Okafor, N. U., & Delaney, D. T. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.08.037 · Cited by 20 · see citing works
Self-citationA Review on Wide Variety and Heterogeneity of IoT PlatformsPreprint
Kollolu, R. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3912454 · Cited by 19 · see citing works
Bayesian Data Fusion: Spatial and Temporal Applications
Fasbender, D., Obsomer, V., Radoux, J., Bogaert, P., & Defourny, P. (2007) · International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) · doi:10.1109/multitemp.2007.4293058 · Cited by 17 · see citing works
Efficient Bayesian Communication Approach for Smart Agriculture Applications
Razafimandimby, C., Loscrì, V., Vegni, A. M., & Neri, A. (2017) · IEEE Vehicular Technology Conference · doi:10.1109/vtcfall.2017.8288147 · Cited by 13 · see citing works
Principles of meteorology and numerical weather prediction
Haupt, S. E., Jiménez, P. A., Lee, J. A., et al. (2017) · Elsevier eBooks · doi:10.1016/b978-0-08-100504-0.00001-9 · Cited by 13 · see citing works
Data Quality Techniques in the Internet of Things: Random Forest Regression
Farooqi, M. M., Khattak, H. A., & Imran, M. (2018) · International Conference on Emerging Technologies · doi:10.1109/icet.2018.8603594 · Cited by 12 · see citing works
An improved weighted fusion algorithm of multi-sensor
Liu, H., Fang, S., & Ji, J. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1453/1/012009 · Cited by 12 · see citing works
A data-driven Kalman filter-PID controller for fibrous capillary irrigation
Abioye, A. E., Abidin, M. S. Z., Mahmud, M. S. A., Buyamin, S., Ijike, O. D., Otuoze, A. O., Ajasa, A. A., & Olajide, O. M. (2022) · Smart Agricultural Technology · doi:10.1016/j.atech.2022.100085 · Cited by 11 · see citing works
Knowledge Graph in Data Quality Evaluation for IoT applications
Khokhlov, I., & Reznik, L. (2020) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot48130.2020.9221091 · Cited by 9 · see citing works
Data fusion algorithm for pulsed eddy current detection
Yang, G., Tian, G. Y., Que, P. W., & Li, Y. (2007) · IET Science Measurement & Technology · doi:10.1049/iet-smt:20060118 · Cited by 8 · see citing works
A Review of Data Quality Assessment: Data Quality Dimensions from User’s Perspective
Abdullah, M. Z., & Arshah, R. A. (2018) · Advanced Science Letters · doi:10.1166/asl.2018.13025 · Cited by 6 · see citing works
5D-IoT, a semantic web based framework for assessing IoT data quality
Mante, S., Hernandez, N., Hussain, A. M., Chaudhari, S., Gangadharan, D., & Monteil, T. (2022) · Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing · doi:10.1145/3477314.3507234 · Cited by 5 · see citing works
Yaohui, Z., Li, W., Bao, S. Z., Haibo, H., & Li, L. (2020) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/570/6/062045 · Cited by 4 · see citing works
Application of Kalman Filter to Large-Scale Geospatial Data
Akatsuka, H., & Terada, M. (2020) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/3397536.3422223 · Cited by 4 · see citing works
Application of Kalman Filter to Large-scale Geospatial Data: Modeling Population Dynamics
Akatsuka, H., & Terada, M. (2022) · ACM Transactions on Spatial Algorithms and Systems · doi:10.1145/3563692 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Bayram, F., Ahmed, B. S., & Hallin, E. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112184 · Cited by 16 · see citing works
A FAIR Perspective on Data Quality Frameworks
Nicholson, N. C., Carvalho, R., & Štotl, I. (2025) · Data · doi:10.3390/data10090136 · Cited by 12 · see citing works
Zhu, Z., Yanghui, F., & Cao, F. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1776697 · Cited by 5 · 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
Enlarging the concept of quality of service: from a sectoral to a multimodal perspective
Ferretto, L., Carra, M., & Barabino, B. (2026) · Transportation Letters · doi:10.1080/19427867.2026.2636006 · Cited by 4 · see citing works
Bamgboye, O., Liu, X., Cruickshank, P., & Liu, Q. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9040108 · Cited by 4 · see citing works
“The continuous increase in the number of connected IoT devices has made it hard to assess the IoT data quality with common assessment methods [11].”
Large-Scale Data Quality Challenges, Framework and Evaluation in Metro Systems
Yuan, T., Xiong, W., & Liu, S. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3474215 · Cited by 3 · see citing works
SLPDBO-BP: an efficient valuation model for data asset value
Zhou, C., Li, S., Xie, C., Yuan, P., & Liao, Z. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2813 · Cited by 3 · see citing works
Towards Trust-Based Data Weighting in Machine Learning
Murphy, S. Ó., Roedig, U., Sreenan, C. J., & Khalid, A. (2023) · IEEE International Conference on Network Protocols (ICNP) · doi:10.1109/icnp59255.2023.10355606 · Cited by 2 · see citing works
Big Data quality assessment in the IoT era
Taleb, I., Dahmani, N., Mathew, S. S., & Hayawi, K. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-21640-4.00003-x · Cited by 2 · see citing works
Semantic 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
A Blockchain-Based Framework for Secure Data Stream Dissemination in Federated IoT Environments
Sychowiec, J., & Zieliński, Z. (2025) · Electronics · doi:10.3390/electronics14102067 · Cited by 3 · see citing works
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
Cunha, Â. S. M., Ribeiro, V. P., Duarte, S. N., Balestieri, J. A. P., Padovani, C. R., Bordignon, Á. J. Z., Maciel, C. D., & Marques, P. A. A. (2026) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2026.107113
Idrees, R., & Amin, M. B. (2026) · Cluster Computing · doi:10.1007/s10586-026-06416-y
Edge AI and IoT for Real-Time Structural Health Monitoring
Hossain, M. A., Mostofa, M. G., Babu, M. A., Hasib, S. N. R., & Arafat, H. (2026) · Journal of Artificial Intelligence and Technological Development · doi:10.59324/jaitd.2026.2(5).07
- Authors
- Byabazaire, J., O’Hare, G. M. P., Collier, R., & Delaney, D.
- Affiliations
- University College Dublin (IE)
- Corresponding author
- Byabazaire, John
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2023-06-28
- Volume
- 23
- Issue
- 13
- Pages
- 5993
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC10346167
- OpenAlex
- W4382726012
- Cited by
- 17
- References
- 54
- Citations by year
- 2026: 8 · 2025: 6 · 2024: 2 · 2023: 1
- FWCI
- 3.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Trinity's Access to Research Output (TARA) (Trinity College Dublin) (submittedVersion, cc-by-nc-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- SFI Strategic Partnership Programme (16/S/3296)
- 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 › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Keywords
- data fusion, adaptive weighted fusion, computational load, scalability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
