Creating time series-based metadata for semantic IoT web services

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Journal Title
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Volume Title
Conference article in proceedings
Date
2018-01-01
Department
Professorship Hyvönen Eero
Department of Computer Science
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Mcode
Degree programme
Language
en
Pages
11
417-427
Series
Database and Expert Systems Applications - 29th International Conference, DEXA 2018, Proceedings, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 11030 LNCS
Abstract
In the near future, the Internet of things (IoT) will rapidly change and automate tasks in our everyday life. IoT networks have sensors measuring the environment and automated agents changing it with respect to predefined objectives. Modeling agents as web services requires lots of metadata from the environment in order to define the desired performance in a specific context. For this purpose, we propose an automatic measurement-based metadata creation method that analyses multivariate time series gathered from the sensors during agents change the environment. The time series analysis uses a cumulative sum algorithm (CuSum) to detect events and association rule learning to find temporal patterns. We evaluate our system with a Long-Term Evolution (LTE) simulator having mobile phones corresponding to IoT devices, LTE macro cells as the data source, and the Self-Organised Network (SON) functions as the automated agents in the network. Our experiments give promising results and show that the metadata creation process can be utilised to characterise IoT agents.
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Citation
Apajalahti , K 2018 , Creating time series-based metadata for semantic IoT web services . in Database and Expert Systems Applications - 29th International Conference, DEXA 2018, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 11030 LNCS , Springer , pp. 417-427 , International Conference on Database and Expert Systems Applications , Regensburg , Germany , 03/09/2018 . https://doi.org/10.1007/978-3-319-98812-2_38