User Privacy in Mobile Advertising

With the pervasiveness of mobile devices in our daily life continuously increasing, mobile advertising is emerging as an important marketing strategy. However, due to its intrusive nature in practice, there has been a growing concern over users’ privacy in mobile advertising, especially push-based mode, which can affect consumers’ acceptance and effectiveness of mobile advertising. Aiming to gain a deeper understanding of not only users’ concerns of privacy intrusion in mobile advertising, but also the potential solutions to addressing those concerns, we conducted a survey in this study. The findings of this study provide a few useful insights for researchers, advertisers, and businesses on both the importance and methods of privacy protection in mobile advertising from a user perspective

Mobile Advertising Using Bluetooth Pull-based Technology

Advertising on mobile devices has large potential due to the very personal and intimate nature of the device and high targeting possibilities. This project is called B.L.U.E system, an advertising system for delivering location-aware mobile advertisement that allow personalization to mobile phones using Bluetooth pull-based technology. This report covers the background study, literature review and theory based on research, and also methodology used in researchand design phase. In research, questionnaire is used to gather information pertaining to user preferences. Meanwhile, in design phase, the methodologies used are Incremental Development and Release; and Assembling Reusable Components. The last two part of this report covers result and discussion from the research and testing phase, and also recommendationfor future work

Machine Learning for Indoor Localization Using Mobile Phone-Based Sensors

In this paper we investigate the problem of localizing a mobile device based on readings from its embedded sensors utilizing machine learning methodologies. We consider a real-world environment, collect a large dataset of 3110 datapoints, and examine the performance of a substantial number of machine learning algorithms in localizing a mobile device. We have found algorithms that give a mean error as accurate as 0.76 meters, outperforming other indoor localization systems reported in the literature. We also propose a hybrid instance-based approach that results in a speed increase by a factor of ten with no loss of accuracy in a live deployment over standard instance-based methods, allowing for fast and accurate localization. Further, we determine how smaller datasets collected with less density affect accuracy of localization, important for use in real-world environments. Finally, we demonstrate that these approaches are appropriate for real-world deployment by evaluating their performance in an online, in-motion experiment.Comment: 6 pages, 4 figure

Success Factors in Mobile Viral Marketing: A Multi-Case Study Approach

A prior study showed that mobile viral marketing is an important issue of mobile marketing. Using a multicase study research approach, we introduce a typology of four standard types of mobile viral marketing and extract eight success factors for this new form of marketing. As a final step, we structure the relationship between both, showing success factors’significance in different standard types and deriving a success factor framework. We conclude with a consideration of research implications.

User-Centered Context-Aware Mobile Applications―The Next Generation of Personal Mobile Computing

Context-aware mobile applications are systems that can sense clues about the situational environment and enable appropriate mechanisms of interaction between end users and systems, making mobile devices more intelligent, adaptive, and personalized. In order to better understand such systems and the potentials and barriers of their development and practical use, this paper provides a state-of-the-art overview of this emerging field. Unlike previous literature reviews that mainly focus on technological aspects of such systems, we examine this field mainly from application and research methodology perspectives. We will present major types of current context-aware mobile applications, and discuss research methodologies used in existing studies and their limitations, and highlight potential future research

Shopping center tracking and recommendation systems

Anacleto R., Luz N., Almeida A., Figueiredo L., Novais P., Shopping Center Tracking and Recommendation Systems, in Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011, Corchado E.; Snasel V., Sedano J., Hassanien A.E.; Calvo J.L., Slezak D. (Eds.), Springer - Series Advances in Intelligent and Soft Computing, vol. 87, ISBN 978-3-642-19643-0, pp 299-308, 2011.Shopping centers present a rich and heterogeneous environment, where IT systems can be implemented in order to support the needs of its actors. However, due to the environment complexity, several feasibility issues emerge when designing both the logical and physical architecture of such systems. Additionally, the system must be able to cope with the individual needs of each actor, and provide services that are easily adopted by them, taking into account several sociological and economical aspects. In this sense, we present an overview of current support systems for shopping center environments. From this overview, a high-level model of the domain (involving actors and services) is described along with challenges and possible features in the context of current Semantic Web, mobile device and sensor technologies

Distributed tuplespace and location management - an integrated perspective using Bluetooth

Location based or "context aware" computing is becoming increasingly recognized as a vital part of a mobile computing environment. As a consequence, the need for location-management middleware is widely recognized and actively researched. Location management is frequently offered to the application through an API where the location is given in the form of coordinates. It is the opinion of the authors that a localization API should offer localized data (e.g. direction to the nearest pharmacy) directly through a transparent and integrated API. Our proposed middleware for location and context management is built on top of Mobispace. Mobispace is a distributed tuplespace made for J2me units where replication between local replicas takes place with a central server (over GPRS) or with other mobile units (using Bluetooth). Since a Bluetooth connection indicates physical proximity to another node, a set of stationary nodes may distribute locality information over Bluetooth connections, and this information may be retrieved through the ordinary tuplespace AP

Mobile-Based Notification System for University's Events

Mobile phone plays a very important role in people life today; its functionality has been extended from voice communication only devices to internet surfing and data transfer. UUM as a higher education institute, hold and organize numerous events throughout the academic year and it relies on email communications for notifying its staff. Using the email notification to announce the staff for the function is suffering from two main problems which are: First, some of the staff do not check his/her email periodically, so they may miss read the notification email about the function and therefore they will not attend the function. Second, sometimes internet service is not available or staffs are at some place where they can not access internet which will lead also to make them unaware about the function or the notification about that function. This study has successfully designed and developed a notification system in order to be used by UUM to send the notifications direct to the staff mobile phones via SMS and thus helps in make sure that the notification is delivered to all interested staff. Successfully implementing this notification system in UUM will provide the university a reliable and convenient inter communication channel