Mobile Data Analytics: A Feedback System for Better Utilization of Sensor-based Devices

Abstract

Mobile devices, including smartphones, are nowadays an essential part of everyday life. They are used worldwide, and across all the demographic groups, they can be utilized for multiple functionalities, including but not limited to communications, such as game playing, social interactions, maps and navigation, leisure, work, and education. With a large on-device sensor base, mobile devices provide a rich source of data. Understanding how these devices are used help us also to increase the knowledge of people’s everyday habits, needs, and rituals. Data collection and analysis can thus be utilized in different recommendation and feedback systems that further increase usage experience of the smart devices. Feedback system computing describes a paradigm where multiple autonomous devices are used together to collect large-scale data. In the case of smartphones, this kind of data can include running and installed applications, different system settings, such as network connection and screen brightness, and various subsystem variables, such as CPU and memory usage. In addition to the autonomous data collection, user questionnaires can be used to provide a wider view to the user community. To understand smart phone usage as a whole, different procedures are needed for cleaning missing and misleading values and preprocessing information from various sets of variables. Analyzing large-scale data sets rising in size to terabytes requires understanding of different Big Data management tools, distributed computing environments, and efficient algorithms to perform suitable data analysis and machine learning tasks. Together, these procedures and methodologies aim to provide actionable feedback, such as recommendations and visualizations, for the benefit of smartphone users, researchers, and application development. This paper provides an approach to a large-scale feedback system mobile analytics. This also describes procedures for cleaning and preprocessing mobile data collected from real-life conditions, such as current system settings and running applications. It shows how interdependencies between different data items are important to consider when analyzing the smartphone system state as a whole. It also provides suitable distributed machine learning and statistical analysis methods for analyzing large-scale mobile data. The algorithms, such as the decision tree-based classification and recommendation system, and information analysis methods presented in this thesis, are implemented in the distributed cloud-computing environment Apache Spark. It provides approaches to generate actionable feedback, such as energy consumption and application recommendations, which can be utilized in the mobile devices themselves or when understanding large crowds of smartphone users. The application areas especially covered in this thesis are smartphone energy consumption analysis in the case of system settings and subsystem variables, trend-based application recommendation system, and analysis of demographic, geographic, and cultural factors in smartphone usage.

Keywords: mobile data, sensor-based devices, cryptic values, smartphones, mutual information

Cite this Article: Vineet Kumar Sharma, Wilson Jeberson. Mobile Data Analytics: A Feedback System for Better Utilization of Sensor-based Devices. International Journal of Mobile Computing Devices. 2019; 5(2): 31–37p.