Mobile devices are ubiquitous, and mobile-generated traffic is arguably a major component of today's web traffic. In particular, the use of smart-phones whilst commuting using public transport is a very popular and common practice in many countries. Mobile commuters however, often suffer from poor performance due to limited bandwidth and/or intermittent network coverage. This paper provides insights into the characteristics of web traffic generated by mobile commuters in these challenged conditions of public transportation systems.We use a dataset collected from 22 Inter-city Buses running on 6 different routes over 5 weeks in Sweden. By analyzing content similarity in time and across different routes, we discover a number of findings that reveal the existence of a spatio-temporal correlation of content popularity and that shed light on diurnal patterns of behavior of mobile commuters. We study popular content accessed by commuters and show that Social Networking and News content are predominant and are mutually exclusive. One of the salient findings is that mobile users' interest on buses is very concentrated, with 35% of the popular content solely accessed on a single day during the 5 weeks, and more than 70% of the popular content from a given day is accessed during one single hour of the day. We also observe high content similarity between specific routes which suggests that content caching within the bus can significantly improve user web experience. Our results indicate that based on the observed strong spatio-temporal correlation of content requests of mobile commuters, caching content inside the buses leads to a daily hit rate ranging from 10 to 20%, with a 20% savings of the daily bandwidth usage.
Abstract-WiFi networks are becoming increasingly ubiquitous. In addition to providing network connectivity, WiFi finds applications in areas such as indoor and outdoor localisation, home automation, and physical analytics. In this paper, we explore the semantics of one key attribute of a WiFi network, SSID name. Using a dataset of approximately 120,000 WiFi access points and their corresponding geo-locations, we use a set of similarity metrics to relate SSID names to known business venues such as cafes, theatres, and shopping centres. Such correlations can be exploited by an adversary who has access to smartphone users preferred networks lists to build an accurate profile of the user and thus can be a potential privacy risk to the users.
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