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 Towards IoT data classification through semantic features
Please use this identifier to cite or link to this item http://hdl.handle.net/10773/21424

title: Towards IoT data classification through semantic features
authors: Antunes, M.
Gomes, Diogo Nuno
Aguiar, R. L.
keywords: IoT
Semantic similarity
Context information
issue date: 2018
publisher: Elsevier
abstract: The technological world has grown by incorporating billions of small sensing devices, collecting and sharing huge amounts of diversified data. As the number of such devices grows, it becomes increasingly difficult to manage all these new data sources. Currently there is no uniform way to represent, share, and understand IoT data, leading to information silos that hinder the realization of complex IoT/M2M scenarios. IoT/M2M scenarios will only achieve their full potential when the devices work and learn together with minimal human intervention. In this paper we discuss the limitations of current storage and analytical solutions, point the advantages of semantic approaches for context organization and extend our unsupervised model to learn word categories automatically. Our solution was evaluated against Miller-Charles dataset and a IoT semantic dataset extracted from a popular IoT platform, achieving a correlation of 0.63.
URI: http://hdl.handle.net/10773/21424
ISSN: 0167-739X
publisher version/DOI: https://doi.org/10.1016/j.future.2017.11.045
source: Future Generation Computer Systems
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