Hybrid machine learning algorithms for fault detection in android smartphones

Abstract

AbstractThe newest trends with smart devices and the concept of Internet of Things (IoT) is that of big data and analytics. More recently, the concept of mobile cloud computing has emerged with advances in terms of hardware and communication speeds. However, there are serious implications on the normal lifetime of the battery‐powered smart devices being used for these different applications. Most sensors deployed for different smart IoT applications are already available in smart phones and used by the majority of individuals on a daily basis. With the recent explosions of the Samsung Galaxy Note 7 smartphones, safety of individuals has climbed the priority list in the IoT field. Along this line, it is important to address fault detection and prediction of smartphones, which are being used in fields such as mobile health. In this paper, a cloud‐based open‐source framework for capturing and processing real‐time streaming information (eg, screen brightness, CPU usage, battery level, voltage, device temperature, and Wi‐Fi signal strength) about different android‐based smartphones has been set up. A NoSQL Cassandra database is used and a Spark distributed computing Scala‐based framework accesses the data for further processing. Classification machine learning algorithms (Naïve‐Bayes (NB), decision tree (DT), and random forest) are used to obtain trained models for predicting symptoms of faulty behaviors in smartphones. The performance of a hybrid 2‐staged machine learning mechanism is proposed whereby cascaded classification algorithms are used. Results show that when using DT algorithm (Level 1) and NB algorithm (Level 2), a slight increase in percentage accuracy is observed. This demonstrates that there is the possibility for further improving the NB model for classification and fault detection when using a combined training model with DT algorithm (Level 1) and NB algorithm (Level 2). The results for the cascaded model show that the percentage accuracy of most hybrid models in this case are slightly inferior to the standalone models themselves. The hybrid models using the DT algorithm at both levels and DT algorithm followed by RF algorithm do not improve the percentage accuracy. However, this demonstrates that the robustness of the classification algorithm can be maintained with this type of hybrid classification algorithm and leaves room for further research of possible techniques and ways to improve the accuracy.