Profiling Wireless Resource Usage for Mobile Apps via Crowdsourcing-Based Network Analytics

The rapid growth of mobile app traffic brings huge pressure to today's cellular networks. While this fact is commonly concerned by all the mobile carriers, little work has been done to analyze app's network resource usage. In this paper, we, for the first time, profile network resource usages for mobile apps by establishing a quantitative mapping between them. We design AppWiR, a crowdsourcing-based mining system that collects app behavior information from phones and mines hundreds of indicators in different network layers. It builds a two-layer causal relationship among app behaviors, network traffics, and network resources. With such relationship knowledge, we model, quantify, and predict the network resource usage for each mobile app. We fully implement the AppWiR crowdsourcing app in Android smartphones to collect data from users. To evaluate its real-world performance, we deploy the AppWiR system and conduct a trial in a leading LTE carrier's network in different geographic areas and network coverages. The trial shows that the AppWiR can accurately estimate and predict the resource usages for mobile apps.