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Wissenschaftliche Vorträge

Human Mobility and Application Usage Prediction Algorithms for Mobile Devices

Verteidigung im Promotionsverfahren von M. Sc. Paul Baumann

19.8.2016, 14:30 Uhr, APB 1004 (Ratssaal)

Mobile devices such as smartphones and smart watches are ubiquitous companions of humans' daily life. As of 2016, there are more mobile devices on Earth than humans. Mobile applications utilize sensors and actuators of these devices to support individuals in their daily life. In particular, 24% of over a million of Android applications leverage users' mobility data. For instance, this data allows the applications to understand which places an individual typically visits to provide her with transportation information, advertisements, or to enable smart home heating systems. These and similar scenarios require the possibility to access the Internet from everywhere and at any time. To realize these scenarios 83% of the applications available in the Android Play Store require the Internet to properly operate and therefore access it from everywhere and at any time. Mobile applications such as Google Now or Apple Siri utilize human mobility data to anticipate where a user will go next or which information she is likely to access en route to her destination. However, predicting human mobility is a challenging task. Existing mobility prediction solutions are typically optimized a priori for a particular application scenario and mobility prediction task. In this sense, there is no approach that allows for automatically composing a mobility prediction solution depending on the underlying prediction task and other parameters. This approach is required to allow mobile devices to support a plethora of mobile applications running on them, while each of the applications targets to support its users by leveraging mobility predictions in a distinct application scenario. These applications rely strongly on the availability of the Internet to work properly. However, mobile cellular network providers are struggling to cope with this fact. Mobile applications generate a monthly average mobile traffic volume that ranged between 1 GB in Asia and 3.7 GB in North America in 2015. The Ericsson Mobility Report Q1 2016 predicts that by the end of 2021 this mobile traffic volume will experience a 12-fold increase. The consequences are higher costs for both providers and consumers, and a reduced quality of service due to congested mobile cellular networks. Several countermeasures can be applied to cope with these problems. For instance, mobile applications apply caching strategies to prefetch application content by predicting which applications will be used next. However, existing solutions suffer from two major shortcomings. They either (1) do not incorporate traffic volume information into their prefetching decisions and thus generate a substantial amount of cellular traffic or (2) require a modification of mobile applications. In this thesis, we present with novel human mobility and application usage prediction algorithms for mobile devices two major contributions that address the aforementioned problems of (1) selecting a human mobility prediction model and (2) prefetching of mobile application content to reduce cellular traffic. First, we address the selection of human mobility prediction models. We report on an exhaustive analysis of the influence of temporal, spatial, and phone context data on the performance of mobility prediction algorithms. Building upon our analysis results, we present (1) SELECTOR -- a novel algorithm for selecting individual human mobility prediction models and (2) MAJOR -- an ensemble learning approach for human mobility prediction. Furthermore, we introduce population mobility models and demonstrate their practical applicability on an analysis of techniques that attempt to detect wrong human mobility predictions. Among these techniques, an ensemble learning based algorithm LOTUS is designed and evaluated. Second, we present EBC -- a novel algorithm for prefetching mobile application content. EBC's goal is to reduce cellular traffic consumption to improve application content freshness. With respect to existing solutions, EBC presents novel techniques (1) to incorporate different strategies for prefetching of mobile applications depending on the available network type and (2) to incorporate application traffic volume predictions into the prefetching decisions. EBC also achieves a reduction in application launch time to the cost of a negligible increase in energy consumption. Developing human mobility and application usage prediction algorithms requires access to the particular human mobility and application usage data. To this end, we leverage in this thesis three publicly available data set. Furthermore, we address the shortcomings of these data sets, namely, (1) the availability of ground-truth mobility data and (2) the availability of human mobility data at short-term events like conferences. We contribute with JK2013 and UbiComp Data Collection Campaign (UbiDCC) two human mobility data sets that address these shortcomings. We also develop and make publicly available a mobile application called LOCATOR that was used to collect our data sets. In summary, the contributions of this thesis provide a step further toward supporting mobile applications and their users. With SELECTOR, we contribute an algorithm that allows optimizing the quality of human mobility predictions by appropriately selecting parameters. To reduce the cellular traffic footprint of mobile applications, we contribute with EBC a novel approach for prefetching of mobile application content by leveraging application usage predictions. Furthermore, we provide insights about how and to which extent wrong and uncertain human mobility predictions can be detected. Lastly, with the mobile application LOCATOR and the two human mobility data sets, we contribute practical tools for researchers in the human mobility prediction domain.

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