Recurrent Neural Networks for Predicting Mobile Device State

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Author(s): Juan Manuel Rodriguez (ISISTAN, UNICEN-CONICET, Argentina), Alejandro Zunino (ISISTAN, UNICEN-CONICET, Argentina), Antonela Tommasel (ISISTAN, UNICEN-CONICET, Argentina)and Cristian Mateos (ISISTAN, UNICEN-CONICET, Argentina)
Copyright: 2019
Pages: 16
Source title: Advanced Methodologies and Technologies in Artificial Intelligence, Computer Simulation, and Human-Computer Interaction
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-5225-7368-5.ch075

Keywords: Artificial Intelligence / Engineering Science Reference / Human-Computer Interaction / Social Sciences & Humanities

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Abstract

Nowadays, mobile devices are ubiquitous in modern life as they allow users to perform virtually any task, from checking e-mails to playing video games. However, many of these operations are conditioned by the state of mobile devices. Therefore, knowing the current state of mobile devices and predicting their future states is a crucial issue in different domains, such as context-aware applications or ad-hoc networking. Several authors have proposed to use different machine learning methods for predicting some aspect of mobile devices' future states. This chapter aims at predicting mobile devices' battery charge, whether it is plugged to A/C, and screen and WiFi state. To fulfil this goal, the current state of a mobile device can be regarded as the consequence of the previous sequence of states, meaning that future states can be predicted by known previous ones. This chapter focuses on using recurrent neural networks for predicting future states.

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