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Energy-efficient prediction of smartphone unlocking

  • Chu LuoEmail author
  • Aku Visuri
  • Simon Klakegg
  • Niels van Berkel
  • Zhanna Sarsenbayeva
  • Antti Möttönen
  • Jorge Goncalves
  • Theodoros Anagnostopoulos
  • Denzil Ferreira
  • Huber Flores
  • Eduardo Velloso
  • Vassilis Kostakos
Original Article
  • 69 Downloads

Abstract

We investigate the predictability of the next unlock event on smartphones, using machine learning and smartphone contextual data. In a 2-week field study with 27 participants, we demonstrate that it is possible to predict when the next unlock event will occur. Additionally, we show how our approach can improve accuracy and energy efficiency by solely relying on software-related contextual data. Based on our findings, smartphone applications and operating systems can improve their energy efficiency by utilising short-term predictions to minimise unnecessary executions, or launch computation-intensive tasks, such as OS updates, in the locked state. For instance, by inferring the next unlock event, smartphones can pre-emptively collect sensor data or prepare timely content to improve the user experience during the subsequent phone usage session.

Keywords

Smartphones Machine learning Sensors Context-awareness 

Notes

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Chu Luo
    • 1
    Email author return OK on get
  • Aku Visuri
    • 2
  • Simon Klakegg
    • 2
  • Niels van Berkel
    • 1
  • Zhanna Sarsenbayeva
    • 1
  • Antti Möttönen
    • 2
  • Jorge Goncalves
    • 1
  • Theodoros Anagnostopoulos
    • 3
  • Denzil Ferreira
    • 2
  • Huber Flores
    • 4
  • Eduardo Velloso
    • 1
  • Vassilis Kostakos
    • 1
  1. 1.The University of MelbourneMelbourneAustralia
  2. 2.University of OuluOuluFinland
  3. 3.University of West AtticaAthensGreece
  4. 4.University of HelsinkiHelsinkiFinland

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