TrCMP: A dependable app usage inference design for user behavior analysis through cyber-physical parameters

Abstract

Abstract Timely and accurate service recommendations can help users improve their quality of life and efficiency of work. The accuracy of service recommendations often depends on effective user behavior analysis. Technically, user behaviors associated with a specific service could be reflected in the apps they use, while operating different apps will change cyber-physical parameters on Android devices, such as power consumption, CPU usage, memory usage, and traffic data. Some studies have already leveraged cyber-physical parameters to analyze app usage. However, these investigations usually take into consideration a single parameter (e.g., traffic or power), which could be easily affected by the complex Android environment. Moreover, to the best of our knowledge, these investigations cannot analyze some apps that run in the offline state. In this paper, we study the similarities and the differences of parameters with various apps running in both online and offline states. Then we propose a design of a dependable app usage inference, named TrCMP to understand the inference in a mobile system through a combination of the cyber and physical system parameters. This design comprehensively considers Tr affic, C PU, M emory and P ower to speculate apps running in both online and offline states. An algorithm is proposed to find the most effective weight values for each parameter. Extensive experiments are conducted to validate the effectiveness and dependability of our design.