The DOI matches its Crossref record; 22 of the 23 references it cites were found in a registry or an index.
Towards Modelling and Reasoning About Uncertain Data of Sensor Measurements for Decision Support in Smart Spaces
Open accessAbstract
Smart Spaces currently benefits from Internet of Things (IoT) infrastructures in order to realise its objective. In many cases, it demonstrates this through certain automated applications that relies on sensor streams that comes with some uncertainties in measurements. However, these sensor data tend to be uncertain or fault-prone due to the faults of the sensor either themselves or the wireless sensor networks. Sometimes, the extreme operating condition of the sensor can be a contributing factor to the uncertainty. The proposed approach provides a software framework that aims at homogenising, annotating and reasoning over these data. The framework consists of four layers that utilizes the semantic process involving a domain ontology and reasoning process to deliver improved quality data streams to applications. This will allow for early detection of missing data points and enhancing the accuracy of decisions and actions in such spaces.
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“The main data quality indicator is associated with uncertainty in measurement.This indicator also conforms to the one identified by [13].”
From Semantic-Driven Approach for Validation of IoT Streaming Data in Trustable Smart City Decision-Making and Monitoring Systems (2025) · cites this paper as [13]
Citations
Cites
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Cited by
Sensor data quality: a systematic review
Teh, H. Y., Kempa-Liehr, A. W., & Wang, K. I.-K. (2020) · Journal of Big Data · doi:10.1186/s40537-020-0285-1 · Cited by 377
Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy
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Lessons Learnt from a Multimodal Learning Analytics Deployment In-the-Wild
Martínez‐Maldonado, R., Echeverría, V., Fernandez-Nieto, G. M., Yan, L., Zhao, L., Alfredo, R., Li, X., Dix, S., Jaggard, H., Wotherspoon, R., et al. (2023) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3622784 · Cited by 66
Hussain, T., Nugent, C., Moore, A., Liu, J., & Beard, A. (2021) · Sensors · doi:10.3390/s21134504 · Cited by 15
An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces
Sharma, S., Gupta, K., Gupta, D., Rani, S., & Dhiman, G. (2023) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.029997 · Cited by 20
Metadata
- Authors
- Bamgboye, O., Liu, X., & Cruickshank, P.
- Affiliations
- Edinburgh Napier University (GB)
- Venue
- 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC)
- Publisher
- IEEE
- Published
- 2018-07
- Pages
- 744-749
- ISSN
- 0730-3157 (ISSN-L)
- OpenAlex
- W2809006397
- Cited by
- 21
- References
- 23
- Citations by year
- 2026: 1 · 2025: 2 · 2023: 2 · 2022: 2 · 2021: 9 · 2020: 1 · 2019: 3 · 2018: 1
- FWCI
- 1.42
- Type
- conference paper
- Language
- en
- Open access
- Licence
- cc-by
- Locations
- IEEE Annual International Computer Software and Applications Conference (COMPSAC) (publishedVersion)
- Research Output (Edinburgh Napier University) (submittedVersion, cc-by)
- Edinburgh Napier Research Repository (Edinburgh Napier University) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
- Keywords
- smart environments, Internet of Things, sensor data uncertainty, data annotation, domain ontology, semantic reasoning, missing data detection, decision support, wireless sensor networks
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