Mobile Web Usage: A Network Perspective

With recent advances in mobile devices and network capabilities, Mobile Internet subscription has caught up to and in some markets even surpassed that of the traditional fixed-line Internet. Hence, in order to sustain future growth and improve their business model, there is a need for the stakeholders to understand the ever evolving Mobile Internet user behaviour. This thesis analysed data collected from a mobile cellular network in Finland during a week in 2010 using a modified version of the Tstat traffc classifier tool to capture HTTP header and network flow data. Since this was the first time this tool was used for the network measurements, the main aim of the thesis was to test the reliability of the data and then to create an analysis process to build in-depth understanding of the traffic usage patterns. Another goal was also to identify mobile handset devices using the new dataset available. First, a study of the traffic symmetry and diurnal pattern of the traffic flow was done, which showed downlink dominating the traffic with periods of high traffic during the evening hours. Comparison with the port-based classification showed that the Tstat traffic classifier was more capable in identifying modern Internet applications correctly. The results also found HTTP to be the dominant protocol in Mobile Internet. These information rich HTTP headers enabled detailed study of the HTTP traffic. The Operating System (OS) information available in the User-Agent (UA) header validated the fact that most traffic is indeed from PC based devices and thus enabled separate study for mobile handset based traffic. For identifying the handsets, the UA headers were mapped to the WURFL database. From this study, Nokia devices were found to have the highest traffic volume and flows followed by the iOS and Android OS platforms. However, there were lot of malformed and non-standard UAs, which means there is a need to further refine the handset identification methodology