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Energy-efficient prediction of smartphone unlocking

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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.

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Authors and Affiliations

  1. The University of Melbourne, Melbourne, Australia

    Chu Luo, Niels van Berkel, Zhanna Sarsenbayeva, Jorge Goncalves, Eduardo Velloso & Vassilis Kostakos

  2. University of Oulu, Oulu, Finland

    Aku Visuri, Simon Klakegg, Antti Möttönen & Denzil Ferreira

  3. University of West Attica, Athens, Greece

    Theodoros Anagnostopoulos

  4. University of Helsinki, Helsinki, Finland

    Huber Flores

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  1. Chu Luo
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  2. Aku Visuri
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Appendix: Parameter tuning

Appendix: Parameter tuning

To improve the performance of SVM, we first tried the linear kernel. However, the performance of SVM with linear kernel degraded (ROCArea = 0.523 at 10 min) compared to the RBF kernel (ROCArea = 0.571 at 10 min). Hence, we attempted to apply parameter tuning methods on the RBF kernel. As illustrated in previous work investigating parameter tuning for SVM with the Radial Basis Function kernel [10, 24] , we repeated the 10-fold cross-validation on the data of 10 min time window with replacing the default setting \(\gamma = \frac {1}{m + 1}\) with a wide variety of γ values.

Figure 20 shows the performance of SVM with the RBF kernel having different γ values. Among all γ assignments, γ = 100 achieved the best performance: ROCArea = 0.813, class “no” precision 0.864, class “no” recall 0.945, class “yes” precision 0.852, class “yes” recall 0.682.

Fig. 20
figure 20

SVM with the Radial Basis Function kernel

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The results indicate that, given a suitable γ, SVM with the RBF kernel can also achieve high performance in the classification to predict unlocking events. However, due to the large number of possible γ values and our limited computing resources, we could not refine our finding, since parameter tuning is highly time-consuming and computation-intensive (in our case, running each γ value took about 10 days for a normal PC). With γ = 100, although SVM with the RBF kernel achieved considerably good results, the performance of Random Forests was still better. Hence, we focused on Random Forests in further analysis. Future work may conduct deeper investigation about the employment of SVM with the RBF kernel in phone unlocking prediction.

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Luo, C., Visuri, A., Klakegg, S. et al. Energy-efficient prediction of smartphone unlocking. Pers Ubiquit Comput 23, 159–177 (2019). https://doi.org/10.1007/s00779-018-01190-0

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