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 Improve IoT/M2M Data Organization Based on Stream Patterns
Please use this identifier to cite or link to this item http://hdl.handle.net/10773/21425

title: Improve IoT/M2M Data Organization Based on Stream Patterns
authors: Antunes, Mário
Jesus, Ricardo
Gomes, Diogo
Aguiar, Rui L.
keywords: Stream Mining
Context awareness
M2M
IoT
Machine learning
issue date: 2017
publisher: IEEE
abstract: The increasing number of small, cheap devices full of sensing capabilities lead to an untapped source of information that can be explored to improve and optimize several systems. Yet, as this number grows it becomes increasingly difficult to manage and organize all this new information. The lack of a standard context representation scheme is one of the main difficulties in this research area. With this in mind we propose a tailored generative stream model, with two main uses: stream similarity and generation. Sensor data can be organized based on pattern similarity, that can be estimated using the proposed model. The proposed stream model will be used in conjunction with our context organization model, in which we aim to provide an automatic organizational model without enforcing specific representations. Moreover, the model can be used to generate streams in a controlled environment. Useful for validating, evaluating and testing any platform that deals with IoT/M2M devices.
URI: http://hdl.handle.net/10773/21425
ISBN: 978-1-5386-2074-8
publisher version/DOI: https://doi.org/10.1109/FiCloud.2017.33
source: Future Internet of Things and Cloud (FiCloud), 2017 IEEE 5th International Conference on
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