Publication
Towards IoT data classification through semantic features
| Summary: | 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. |
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| Main Authors: | Antunes, M. |
| Other Authors: | Gomes, Diogo Nuno; Aguiar, R. L. |
| Subject: | IoT Semantic similarity Context information M2M |
| Year: | 2018 |
| Country: | Portugal |
| Document type: | article |
| Access type: | open access |
| Associated institution: | Universidade de Aveiro |
| Language: | English |
| Origin: | RIA - Repositório Institucional da Universidade de Aveiro |
| Summary: | 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. |
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