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How Machine Learning Could Detect Anomalies on Thinger.io Platform?

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 887)

Abstract

This research explores the capacity of Machine Learning techniques to detect anomalies and how incorporate this capacity to thinger.io platform. Thinger.io is a IoT opensource platform that allows to create an IoT environment using any hardware available on market. In this paper, several ML techniques are proposed to detect anomalies in the platform.

Keywords

Predictive analysis Machine Learning Time series IoT 

Notes

Acknowledgements

This work was supported in part by Project MINECO TEC2017-88048-C2-2-R, FAPERJ APQ1 Project 211.500/2015, FAPERJ APQ1 Project 211.451/2015, CNPq Universal 430082/2016-9, FAPERJ JCNE E-26/203.287/2017, Project Prociência 2017-038625-0, CNPq PQ 312792/2017-4.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Mathematics and StatisticsRio de Janeiro State UniversityRio de JaneiroBrazil
  2. 2.Institute of ComputingFluminense Federal UnivertityNiteróiBrazil
  3. 3.Computer Science DepartmentCarlos III University of MadridMadridSpain

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