8,566 research outputs found
Location-based Marketing: the academic framework
Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Over the last several years one could observe revolution in location-based
technologies and geospatial information. Location awareness of mobile devices
resulted in development of Location-Based Services (LBS) that are realization of that
revolution in the most personal and contextual way. The ability to reach consumers
in the highly targeted manner based on spatio-temporal criteria, attracted marketers
from the early beginning of LBS creating field called Location-Based Marketing.
Today decreasing prices of smartphones and wireless internet, as well as integration
of location-aware mobile solutions and social media is leading to new possibilities
and opportunities. The academic and professional interests of the author made him noticed that
although the industry has challenged a significant development, there is lack of
publications that would put an academic framework on that progress. The research
has fulfilled this gap by extensive investigation of the current state of the art of
Location-Based Marketing and its foundations - Location Based Services.
The dissertation provides academic framework by comprehensive analysis of the
Location-Based Marketing from LBS and marketing perspective. Further the thesis is
addressing the issue of significant discrepancy between theoretical concepts of
measurable Location-Based Social Media data and the actual data than can be legally
accessed and used for marketing analysis purposes by investigation a case study of
Location-Based Social Network - Foursqaure and Location-Based Analytics platform
VenueLabs
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
Location Privacy Protection in the Mobile Era and Beyond
As interconnected devices become embedded in every aspect of our lives, they accompany
many privacy risks. Location privacy is one notable case, consistently recording an individual’s
location might lead to his/her tracking, fingerprinting and profiling. An individual’s
location privacy can be compromised when tracked by smartphone apps, in indoor spaces,
and/or through Internet of Things (IoT) devices. Recent surveys have indicated that users
genuinely value their location privacy and would like to exercise control over who collects
and processes their location data. They, however, lack the effective and practical tools to
protect their location privacy. An effective location privacy protection mechanism requires
real understanding of the underlying threats, and a practical one requires as little changes to
the existing ecosystems as possible while ensuring psychological acceptability to the users.
This thesis addresses this problem by proposing a suite of effective and practical privacy
preserving mechanisms that address different aspects of real-world location privacy threats.
First, we present LP-Guardian, a comprehensive framework for location privacy protection
for Android smartphone users. LP-Guardian overcomes the shortcomings of existing
approaches by addressing the tracking, profiling, and fingerprinting threats posed by
different mobile apps while maintaining their functionality. LP-Guardian requires modifying
the underlying platform of the mobile operating system, but no changes in either the apps
or service provider. We then propose LP-Doctor, a light-weight user-level tool which allows
Android users to effectively utilize the OS’s location access controls. As opposed to
LP-Guardian, LP-Doctor requires no platform changes. It builds on a two year data collection
campaign in which we analyzed the location privacy threats posed by 1160 apps for
100 users. For the case of indoor location tracking, we present PR-LBS (Privacy vs. Reward
for Location-Based Service), a system that balances the users’ privacy concerns and
the benefits of sharing location data in indoor location tracking environments. PR-LBS
fits within the existing indoor localization ecosystem whether it is infrastructure-based
or device-based. Finally, we target the privacy threats originating from the IoT devices
that employ the emerging Bluetooth Low Energy (BLE) protocol through BLE-Guardian.
BLE-Guardian is a device agnostic system that prevents user tracking and profiling while
securing access to his/her BLE-powered devices. We evaluate BLE-Guardian in real-world
scenarios and demonstrate its effectiveness in protecting the user along with its low overhead
on the user’s devices.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138563/1/kmfawaz_1.pd
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