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

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• 作者：Daniela Ventura (19)
  Diego Casado-Mansilla (20)
  Juan López-de-Armentia (20)
  Pablo Garaizar (20)
  Diego López-de-Ipi?a (20)
  Vincenzo Catania (19)
  
• 关键词：IoT ; RESTful Infrastructure ; Machine Learning ; ARIMA Models ; Eco ; aware Everyday Things ; Energy Efficiency ; Coffee ; Maker
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8867
• 期：1
• 页码：444-451
• 全文大小：765 KB
• 参考文献：1. The LinkSmart Project (August 2014), http://www.hydramiddleware.eu/
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  4. Gao, L., Zhang, C., et al.: RESTful Web of Things API in sharing sensor data. In: Proc. of ICITST 2011, pp. 1- (2011)
  5. Wang, H.-I.: Constructing the Green Campus within the Internet of Things Architecture. Journal of Distributed Sensor Networks, 1- (2014)
  6. Weiss, M., Guinard, D.: Increasing Energy Awareness Through Web-enabled Power Outlets. In: MUM 2010, pp. 20-0 (2010)
  7. López-de-Armentia, J., Casado-Mansilla, D., López-de-Ipi?a, D.: Reducing energy waste through eco-aware every-day things. Journal of MIS?10(1) (2014)
  8. Seung-Seok, C., et al.: A Survey of Binary Similarity and Distance Measures. Journal of Systemics, Cybernetics and Informatics?8(1) (2010)
  
• 作者单位：Daniela Ventura (19)
  Diego Casado-Mansilla (20)
  Juan López-de-Armentia (20)
  Pablo Garaizar (20)
  Diego López-de-Ipi?a (20)
  Vincenzo Catania (19)
  
  19. University of Catania, Catania, Italy
  20. Deusto Institute of Technology - DeustoTech, University of Deusto, Bilbao, Spain
  
• ISSN：1611-3349

文摘

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.