IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services
Open accessAbstract
With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic models for stream annotation are scarce, as generally, semantics are heavy to process and not ideal for Internet of Things (IoT) environments, where the data are frequently updated. We present a light model to semantically annotate streams, IoT-Stream. It takes advantage of common knowledge sharing of the semantics, but keeping the inferences and queries simple. Furthermore, we present a system architecture to demonstrate the adoption the semantic model, and provide examples of instantiation of the system for different use cases. The system architecture is based on commonly used architectures in the field of IoT, such as web services, microservices and middleware. Our system approach includes the semantic annotations that take place in the pipeline of IoT services and sensory data analytics. It includes modules needed to annotate, consume, and query data annotated with IoT-Stream. In addition to this, we present tools that could be used in conjunction to the IoT-Stream model and facilitate the use of semantics in IoT.
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Traditional data validation methods are insufficient for handling the dynamic and heterogeneous nature of IoT data streams.
“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 [7]
The Introduction (page unavailable) describes the changing volume, velocity, sources, and formats of IoT data as challenges. Related Work (page unavailable) discusses limitations of existing stream-annotation models, but the paper does not establish that traditional data validation methods are insufficient. (LLM, conf=0.82)
Experimental — accuracy not yet measuredIt further establishes that the semantic stream processing can still be contained within the interval of IoT streaming data generation of sensor reads in smart city application scenarios, such as air quality monitoring and smart transportation [7,21].
“It further establishes that the semantic stream processing can still be contained within the interval of IoT streaming data generation of sensor reads in smart city application scenarios, such as air quality monitoring and smart transportation [7,21].”
From Semantic-Driven Approach for Validation of IoT Streaming Data in Trustable Smart City Decision-Making and Monitoring Systems (2025) · cites this paper as [7]
Section 5.1, “Smart City Traffic and Environment,” describes traffic and air-quality streams whose sensors generate data every five minutes, along with an example analytics workflow. The Conclusions say the lightweight model improves stream-query processing time, but our copy does not establish that semantic processing finishes within the interval between sensor readings. (LLM, conf=0.82)
Experimental — accuracy not yet measuredA probabilistic model such as Machine Learning does not support data interoperability requirements for data-driven smart systems and is unable to provide runtime reasoning as well as simultaneously analyse data produced from heterogeneous sources.
“It does not support data interoperability requirements for data-driven smart systems and is unable to provide runtime reasoning as well as simultaneously analyse data produced from heterogeneous sources [7].”
From Semantic-Driven Approach for Validation of IoT Streaming Data in Trustable Smart City Decision-Making and Monitoring Systems (2025) · cites this paper as [7]
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- Authors
- Elsaleh, T., Enshaeifar, S., Rezvani, R., Acton, S. T., Janeiko, V., & Bermudez-Edo, M.
- Affiliations
- University of Surrey (GB)
- University of St Andrews (GB)
- Software Engineering Department, University of Granada, 18012 Granada, Spain
- Corresponding author
- Elsaleh, Tarek
- ORCID
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-02-11
- Volume
- 20
- Issue
- 4
- Pages
- 953
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7071512
- OpenAlex
- W3006424452
- Cited by
- 96
- References
- 41
- Citations by year
- 2026: 4 · 2025: 16 · 2024: 10 · 2023: 12 · 2022: 22 · 2021: 24 · 2020: 8
- FWCI
- 6.49
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by)
- Surrey Research Insight Open Access (The University of Surrey) (submittedVersion)
- Institutional Repository of the University of Granada (University of Granada) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Horizon 2020 Framework Programme (779852, 732679)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
- Keywords
- lightweight ontologies, Internet of Things, semantic annotation, event detection, microservices, middleware, web services, IoT architecture
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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.
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