Mobile data traffic is growing exponentially and it is even more challenging to distribute content efficiently while users are “on the move” such as in public transport. The use of mobile devices for accessing content (e.g., videos) while commuting are both expensive and unreliable, although it is becoming common practice worldwide. Leveraging on the spatial and temporal correlation of content popularity and users' diverse network connectivity, we propose a novel content distribution system, uStash, which guarantees better QoE with regards to access delays and cost of usage. The proposed collaborative download and content stashing schemes provide the uStash provider the flexibility to control the cost of content access via cellular networks. We model the uStash system in a probabilistic framework and thereby analytically derive the optimal portions for collaborative downloading. Then, we validate the proposed models using real-life trace driven simulations. In particular, we use dataset from 22 inter-city buses running on six different routes and from a mobile VoD service provider to show that uStash reduces the cost of monthly cellular data by approximately 50 percent and the expected delay for content access by 60 percent compared to content downloaded via users' cellular network connections.

Accurate estimation of crowd dynamics is difficult, especially when it comes to fine-grained spatial and temporal predictions. A deep understanding of these fine-grained dynamics is crucial during a major disaster, as it guides efficient disaster managements. However, it is particularly challenging as these fine-grained dynamics are mainly caused by high-dimensional individual movement and evacuation. Furthermore, abnormal user behavior during disasters makes the problem of accurate prediction even more acute. Traditional models have difficulties in dealing with these high dimensional patterns caused by disruptive events. For example, the 2016 Kumamoto earthquakes disrupted normal crowd dynamics patterns significantly in the affected regions. We first perform a thorough analysis of a crowd population distribution dataset during Kumamoto earthquakes collected by a major mobile network operator in Japan, which shows strong fine-grained temporal autocorrelation and spatial correlation among geographically neighboring grids. It is also demonstrated that temporal autocorrelation during disasters is more than simple diurnal patterns. Moreover, there are many factors that could potentially influence spatial correlations and affect the dynamics patterns. Then, we illustrate how a spatial-temporal Long-Short-Term-Memory (LSTM) deep neural network could be applied to boost the prediction power. It is shown that the error in terms of Mean Square Error (MSE) is reduced by as much as 55.1-69.4% compared to regressive models such as AR, ARIMA and SVR. Furthermore, LSTM outperforms the aforementioned models significantly even when little training data is available right after the mainshock. Finally, we also show a Region-aware LSTM does not necessarily outperform a regular LSTM.

Mobile video traffic is exploding and it is particularly challenging to stream video when high density of users are "on the move", e.g., in public transport systems. It becomes increasingly problematic as video traffic is predicted to account for more than 80% of Internet traffic by 2019. This will be exacerbated by factors such as cellular network coverage issues and unstable network throughput due to high speed mobility. By exploiting the predictable public transport mobility patterns, spatio-temporal correlation of user interests and users' video viewing behaviors, we proposed TransFetch which uses intelligent caching on-board the public transport vehicles as well as a novel video chunk placement algorithm. We show through extensive simulations, that TransFetch reduces the system cellular data usage by up to 45% and improves the quality of video streaming by up to 35%. Finally, we demonstrate the practical feasibility of TransFetch by implementing caching units on a Raspberry-Pi and a mobile app on an Android device.

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.

Mobile device users use applications that require text input. Today there are three primary text input modalities, soft keyboard (SK), speech to text (STT) and Swype. Each of these input modalities have different energy demands, and as a result, their use will have a significant impact on the battery life of the mobile device. Using high-precision power measurement hardware and systematically taking into account the user context, we characterize and compare the energy consumption of these three text input modalities. We show that the length of interaction determines the most energy efficient modality. If the interactions is short, on average less than 30 characters, using the device SK is the most energy efficient. For longer interactions, the use of a STT applications is more energy efficient. Swype is more energy efficient than STT for very short interactions, less than 5 characters on average, but is never as efficient as SK. This is primarily due to STT enabling the users to complete tasks more quickly than when using SK or Swype. We also show that these results are independent of “user style”, the experience of using different input modalities and device characteristics. Finally we show that STT energy efficiency is dependent on application logic of whether speech samples are for a given period of time before transmitting to a server for analysis as opposed to streaming the speech to a sever for analysis. Based on these observations we recommend that the users should use SK for short interactions of less than 30 characters, and STT for longer interactions. In addition, they should use STT applications which uses storing and transmit logic, if they are willing to trade off battery life to QoE. Finally we proposed the development of an adaptive storing and analyze STT to improve the energy efficiency of it.

Crowd-Cache is a novel crowd-sourced content caching system which provides cheap and convenient content access for mobile users. Our system exploits both transient colocation of devices and the spatial temporal correlation of content popularity, where users in a particular location and at specific times would be likely interested in similar content. We demonstrate the feasibility of Crowd-Cache system through a prototype implementation on Android smartphones.