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Information 2019, 10(3), 86; https://doi.org/10.3390/info10030086

Towards Integrating Mobile Devices into Dew Computing: A Model for Hour-Wise Prediction of Energy Availability

1
Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90007, USA
2
ISISTAN-UNICEN-CONICET, Tandil B7001BBO, Buenos Aires, Argentina
*
Author to whom correspondence should be addressed.
Received: 17 December 2018 / Revised: 20 February 2019 / Accepted: 21 February 2019 / Published: 26 February 2019
(This article belongs to the Special Issue Information Technology: New Generations (ITNG 2018))
Full-Text   |   PDF [1078 KB, uploaded 26 February 2019]   |  

Abstract

With self-provisioning of resources as premise, dew computing aims at providing computing services by minimizing the dependency over existing internetwork back-haul. Mobile devices have a huge potential to contribute to this emerging paradigm, not only due to their proximity to the end user, ever growing computing/storage features and pervasiveness, but also due to their capability to render services for several hours, even days, without being plugged to the electricity grid. Nonetheless, misusing the energy of their batteries can discourage owners to offer devices as resource providers in dew computing environments. Arguably, having accurate estimations of remaining battery would help to take better advantage of a device’s computing capabilities. In this paper, we propose a model to estimate mobile devices battery availability by inspecting traces of real mobile device owner’s activity and relevant device state variables. The model includes a feature extraction approach to obtain representative features/variables, and a prediction approach, based on regression models and machine learning classifiers. On average, the accuracy of our approach, measured with the mean squared error metric, overpasses the one obtained by a related work. Prediction experiments at five hours ahead are performed over activity logs of 23 mobile users across several months. View Full-Text
Keywords: mobile cloud computing; dew computing; battery prediction; feature selection; machine learning mobile cloud computing; dew computing; battery prediction; feature selection; machine learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Longo, M.; Hirsch, M.; Mateos, C.; Zunino, A. Towards Integrating Mobile Devices into Dew Computing: A Model for Hour-Wise Prediction of Energy Availability. Information 2019, 10, 86.

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