Please use this identifier to cite or link to this item:
Title: Improve IoT/M2M Data Organization Based on Stream Patterns
Author: Antunes, Mário
Jesus, Ricardo
Gomes, Diogo
Aguiar, Rui L.
Keywords: Stream Mining
Context awareness
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.
Peer review: yes
DOI: 10.1109/FiCloud.2017.33
ISBN: 978-1-5386-2074-8
Appears in Collections:DETI - Comunicações
IT - Comunicações

Files in This Item:
File Description SizeFormat 
paper.pdf904.03 kBAdobe PDFView/Open

Formato BibTex MendeleyEndnote Degois 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.