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ARIIMA: A Real IoT Implementation of a Machine-Learning Architecture for Reducing Energy Consumption

  • Daniela Ventura
  • Diego Casado-Mansilla
  • Juan López-de-Armentia
  • Pablo Garaizar
  • Diego López-de-Ipiña
  • Vincenzo Catania
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8867)

Abstract

As the inclusion of more devices and appliances within the IoT ecosystem increases, methodologies for lowering their energy consumption impact are appearing. On this field, we contribute with the implementation of a RESTful infrastructure that gives support to Internet-connected appliances to reduce their energy waste in an intelligent fashion. Our work is focused on coffee machines located in common spaces where people usually do not care on saving energy, e.g. the workplace. The proposed approach lets these kind of appliances report their usage patterns and to process their data in the Cloud through ARIMA predictive models. The aim such prediction is that the appliances get back their next-week usage forecast in order to operate autonomously as efficient as possible. The underlying distributed architecture design and implementation rationale is discussed in this paper, together with the strategy followed to get an accurate prediction matching with the real data retrieved by four coffee machines.

Keywords

IoT RESTful Infrastructure Machine Learning ARIMA Models Eco-aware Everyday Things Energy Efficiency Coffee-Maker 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Daniela Ventura
    • 1
  • Diego Casado-Mansilla
    • 2
  • Juan López-de-Armentia
    • 2
  • Pablo Garaizar
    • 2
  • Diego López-de-Ipiña
    • 2
  • Vincenzo Catania
    • 1
  1. 1.University of CataniaCataniaItaly
  2. 2.Deusto Institute of Technology - DeustoTechUniversity of DeustoBilbaoSpain

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