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IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services

Elsaleh, Tarek, Enshaeifar, Shirin, Rezvani, Roonak, Acton, Sahr Thomas, Janeiko, Valentinas and Bermudez-Edo, Maria (2020) IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services Sensors, 20 (4).

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Abstract

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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Elsaleh, TarekT.Elsaleh@surrey.ac.uk
Enshaeifar, Shirins.enshaeifar@surrey.ac.uk
Rezvani, Roonakr.rezvani@surrey.ac.uk
Acton, Sahr Thomas
Janeiko, Valentinasv.janeiko@surrey.ac.uk
Bermudez-Edo, Maria
Date : 11 February 2020
Funders : European Union's Horizon 2020
DOI : 10.3390/s20040953
Grant Title : IoTCrawler
Copyright Disclaimer : This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Uncontrolled Keywords : IoT; Data model; Ontology; data stream; Semantic model; Linked data
Depositing User : Clive Harris
Date Deposited : 03 Sep 2020 18:34
Last Modified : 03 Sep 2020 18:34
URI: http://epubs.surrey.ac.uk/id/eprint/858533

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