20,492 research outputs found
Human Mobility and Application Usage Prediction Algorithms for Mobile Devices
Mobile devices such as smartphones and smart watches are ubiquitous companions of humans’ daily life. Since 2014, 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 the Android applications leverage users’ mobility data. For instance, this data allows applications to understand which places an individual typically visits. This allows providing her with transportation information, location-based 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 operate properly 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. 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 support its users by leveraging mobility predictions in a distinct application scenario.
Mobile applications rely strongly on the availability of the Internet to work properly. However, mobile cellular network providers are struggling to provide necessary cellular resources. 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 application code.
In this thesis, we present novel human mobility and application usage prediction algorithms for mobile devices. These two major contributions 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 extensive 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. In particular, we analyze techniques that focus on detection of wrong human mobility predictions. Among these techniques, an ensemble learning algorithm, called 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 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 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 lack of ground-truth mobility data and (2) the lack 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, which was used to collect our data sets.
In summary, the contributions of this thesis provide a step further towards 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 what extent wrong and uncertain human mobility predictions can be detected. Lastly, with our mobile application LOCATOR and two human mobility data sets, we contribute practical tools for researchers in the human mobility prediction domain
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
Performance Analysis of Multicast Mobility in a Hierarchical Mobile IP Proxy Environment
Mobility support in IPv6 networks is ready for release as an RFC, stimulating
major discussions on improvements to meet real-time communication requirements.
Sprawling hot spots of IP-only wireless networks at the same time await voice
and videoconferencing as standard mobile Internet services, thereby adding the
request for multicast support to real-time mobility. This paper briefly
introduces current approaches for seamless multicast extensions to Mobile IPv6.
Key issues of multicast mobility are discussed. Both analytically and in
simulations comparisons are drawn between handover performance characteristics,
dedicating special focus on the M-HMIPv6 approach.Comment: 11 pages, 7 figure
Mining large-scale human mobility data for long-term crime prediction
Traditional crime prediction models based on census data are limited, as they
fail to capture the complexity and dynamics of human activity. With the rise of
ubiquitous computing, there is the opportunity to improve such models with data
that make for better proxies of human presence in cities. In this paper, we
leverage large human mobility data to craft an extensive set of features for
crime prediction, as informed by theories in criminology and urban studies. We
employ averaging and boosting ensemble techniques from machine learning, to
investigate their power in predicting yearly counts for different types of
crimes occurring in New York City at census tract level. Our study shows that
spatial and spatio-temporal features derived from Foursquare venues and
checkins, subway rides, and taxi rides, improve the baseline models relying on
census and POI data. The proposed models achieve absolute R^2 metrics of up to
65% (on a geographical out-of-sample test set) and up to 89% (on a temporal
out-of-sample test set). This proves that, next to the residential population
of an area, the ambient population there is strongly predictive of the area's
crime levels. We deep-dive into the main crime categories, and find that the
predictive gain of the human dynamics features varies across crime types: such
features bring the biggest boost in case of grand larcenies, whereas assaults
are already well predicted by the census features. Furthermore, we identify and
discuss top predictive features for the main crime categories. These results
offer valuable insights for those responsible for urban policy or law
enforcement
Anticipating Information Needs Based on Check-in Activity
In this work we address the development of a smart personal assistant that is
capable of anticipating a user's information needs based on a novel type of
context: the person's activity inferred from her check-in records on a
location-based social network. Our main contribution is a method that
translates a check-in activity into an information need, which is in turn
addressed with an appropriate information card. This task is challenging
because of the large number of possible activities and related information
needs, which need to be addressed in a mobile dashboard that is limited in
size. Our approach considers each possible activity that might follow after the
last (and already finished) activity, and selects the top information cards
such that they maximize the likelihood of satisfying the user's information
needs for all possible future scenarios. The proposed models also incorporate
knowledge about the temporal dynamics of information needs. Using a combination
of historical check-in data and manual assessments collected via crowdsourcing,
we show experimentally the effectiveness of our approach.Comment: Proceedings of the 10th ACM International Conference on Web Search
and Data Mining (WSDM '17), 201
Towards Psychometrics-based Friend Recommendations in Social Networking Services
Two of the defining elements of Social Networking Services are the social
profile, containing information about the user, and the social graph,
containing information about the connections between users. Social Networking
Services are used to connect to known people as well as to discover new
contacts. Current friend recommendation mechanisms typically utilize the social
graph. In this paper, we argue that psychometrics, the field of measuring
personality traits, can help make meaningful friend recommendations based on an
extended social profile containing collected smartphone sensor data. This will
support the development of highly distributed Social Networking Services
without central knowledge of the social graph.Comment: Accepted for publication at the 2017 International Conference on AI &
Mobile Services (IEEE AIMS
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