Energy-Efficient Prediction of Smartphone Unlocking (bibtex)
by Luo, Chu, Visuri, Aku, Klakegg, Simon, van Berkel, Niels, Sarsenbayeva, Zhanna, Möttönen, Antti, Goncalves, Jorge, Anagnostopoulos, Theodoros, Ferreira, Denzil, Flores, Huber, Velloso, Eduardo and Kostakos, Vassilis
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.
Reference:
C. Luo, A. Visuri, S. Klakegg, N. van Berkel, Z. Sarsenbayeva, A. Möttönen, J. Goncalves, T. Anagnostopoulos, D. Ferreira, H. Flores, E. Velloso, V. Kostakos, "Energy-Efficient Prediction of Smartphone Unlocking", Personal and Ubiquitous Computing, vol. 23, no. 1, 2019, 159-177.
Bibtex Entry:
@article{Luo2019PredictUnlock,
	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.},
	Author = {Luo, Chu and Visuri, Aku and Klakegg, Simon and van Berkel, Niels and Sarsenbayeva, Zhanna and Möttönen, Antti and Goncalves, Jorge and Anagnostopoulos, Theodoros and Ferreira, Denzil and Flores, Huber and Velloso, Eduardo and Kostakos, Vassilis},
	Doi = {10.1007/s00779-018-01190-0},
	Journal = {Personal and Ubiquitous Computing},
	Number = {1},
	Pages = {159-177},
	Title = {Energy-Efficient Prediction of Smartphone Unlocking},
	Type = {Journal Article},
	Url = {https://nielsvanberkel.com/files/publications/puc2019a.pdf},
	Volume = {23},
	Year = {2019}}
Powered by bibtexbrowser