Thesis (Ph.D.) - University of Glasgow, 2008. / Ph.D. thesis submitted to the Faculty of Information and Mathematical Sciences, Department of Computing Science, University of Glasgow, 2008. Includes bibliographical references. Print version also available.

As ubiquitous computing (ubicomp) technologies reach maturity, smart phones and context-based services are gaining mainstream popularity. A smart phone accompanies its user throughout (nearly) all aspects of his life, becoming an indispensable assistant the busy user relies on to help navigate his life, using map applications to navigate the physical world, email and instant messaging applications to keep in touch, media player applications to be entertained, etc. As a smart phone is capable of sensing the physical and virtual context of the user with an array of “hard” sensors (e.g., GPS, accelerometer) and “soft” sensors (e.g., email, social network, calendar),it is well-equipped to tailor the assistance it provides to the user. Over the life of a smart phone, it is entrusted with an enormous amount of personal information, everything from context-information sensed by the phone to contact lists to call-logs to passwords. Based on this rich set of information it is possible to model the behavior of the user, and use the models to detect anomalies (i.e., significant variations) in the user’s behavior. Anomaly detection capabilities enable a variety of application domains such as device theft detection, improved authentication mechanisms, impersonation, prevention, physical emergency detection, remote elder-care monitoring, and other proactive services. There has been extensive prior research on anomaly detection in various application domain areas (e.g., fraud detection, intrusion detection). Yet these approaches cannot be used in ubicomp environments as 1) they are very application-specific and not versatile enough to learn complex day to day behavior of users, 2) they work with a very small number of information sources with a relatively uniform stream of information (unlike sensor data from mobile devices), and 3) most approaches require labeled or semi-labeled data about anomalies (in ubicomp environments, it is very costly to create labeled datasets). Existing work in the field of anomaly detection in ubicomp environments is quite sparse. Most of the existing work focuses on using a single sensor information stream (GPS in most cases) to detect anomalies in the user’s behavior. However there exists a somewhat richer vein of prior work in modeling user behavior with the goal of behavior prediction; this is again limited mostly to a single sensor stream or single type of prediction (mostly location). This dissertation presents the notion of modeling mobile user behavior as a collection of models each capturing an aspect of the user’s behavior such as indoor mobility, typing patterns, calling patterns. A novel mechanism is developed for combining these models (i.e.,CobLE), which operate on asynchronous information sources from the mobile device, taking into consider how well each model is estimated to perform in the current context. These ideas are concretely implemented in an extensible framework, McFAD. Evaluations carried out using real-world datasets on this framework in contrast to prior work show that the framework for detecting anomalous behavior, 1) vastly reduces the training data requirement, 2) increases coverage, and 3) dramatically increases performance.

GoCity is designed to provide city visitors with up-to-date and context-aware information while they are exploring a city using Android mobile phones. This thesis not only introduces the design and analysis of GoCity, but also discusses four problems in leveraging three concepts—context-awareness, self-adaptation, and usability—in current mobile application design. First, few contexts other than location and time have been used in actual mobile applications. Second, there is no clear classification of context information for mobile application design. Third, mobile application designers lack systematic mechanisms to address sensing and monitoring requirements under changing context situations. This is crucial for effective self-adaptation. Fourth, most mobile applications have low usability due to poor user interface (UI) design. The model proposed in this thesis addresses these issues by (i) supporting diverse context dimensions, (ii) monitoring context changes continuously and tailoring the application behavior according to these changes, and (iii) improving UI design using selected usability methods. In addition, this thesis proposes two classifications of context information for mobile applications: source-based classification—personal context, mobile device context, and environmental context; and property-based classification—static context and dynamic context. The combination of these two classifications helps determine the observed context and its polling rate—the rate at which the context is collected—effectively. A distinctive feature of GoCity is that it supports two interaction modes—static mode and dynamic mode. In static mode, the application generates results only after the user sends the request to it. In other words, it does not actively generate results for users. In contrast, in the dynamic mode, the application continuously updates results even if the user does not send any request to it. The notion of an autonomic element (AE) is used for the dynamic mode to make GoCity self-adaptive. The polling rates on different contexts are also handled differently in the dynamic mode because of the differences among context properties. In addition, GoCity is composed of, but not limited to, four sub-applications. Each sub-application employs a variety of context information and can be implemented as an independent mobile application. Regarding usability, GoCity focuses on providing a simple and clear user interface as well as supporting user expectations for personalization. An experiment which involves a person visiting the city of Victoria was conducted to evaluate GoCity. In this evaluation, three determining factors of usability were employed to qualitatively and quantitatively assess GoCity. In addition, the static mode and dynamic mode were evaluated separately. / Graduate

This thesis presents the design and implementation of I-Shop, a context-aware, shopping smartphone application designed to provide shoppers with relevant advertisements for product and services available in close proximity. We argue that current context-aware mobile applications exhibit significant limitations in the following domains: (1) use of context, (2) invasion of privacy, (3) spam management, and (4) platform dependency. The proposed context model attempts to tackle these shortcomings by exploiting available contextual information from social media networks such as Facebook. Our goal is to use a user’s personal information, such as their native language and personal interests, to direct the most relevant advertisements to them. To alleviate any privacy issues, a user’s personal information is never sent out to any back-end services and only apply the filters locally. In addition, unlike most other predictive approaches that track the user’s location history, we follow a reactive approach which triggers only when the user is close to a shopping area. When a user arrives to a particular shopping area, the application asks whether she wishes to view any advertisements of local products and services. Upon approval, the application retrieves deals on products including services sorted by domain from databases, such as Groupon and our custom extended deals database. Finally, the application filters the retrieved data according to personal interests and then displays the results. As a proof of concept, we designed and implemented the I-Shop prototype application. We built I-Shop as a hybrid application using IBM’s state-of-the-art Worklight infrastructure. This approach lets developers optimize their time and effort; enabling a “write once, deploy everywhere” development model that not only reduces development costs but also increases application performance by providing a combination of native and web capabilities. In addition, I-Shop also leverages several features offered by the IBM Worklight infrastructure including cross-platform support, direct update, internalization, and integration of third-party libraries and toolkits. / Graduate

Thesis (Ph. D.)--University of Hong Kong, 2009. / Includes bibliographical references (p. 135-151) Also available in print.

Thesis--Auburn University, 2005. / Abstract. Vita. Includes bibliographic references (p.63-67).

Thesis (Ph. D.)--University of North Texas, Dec., 2009. / Title from title page display. Includes bibliographical references.

Thesis (Ph.D.)--University of Nebraska-Lincoln, 2008. / Title from title screen (site viewed Nov. 25, 2008). PDF text: xiii, 173 p. : ill. ; 2 Mb. UMI publication number: AAT 3315261. Includes bibliographical references. Also available in microfilm and microfiche formats.

Context-aware computing enables mobile applications to discover and benefit from valuable context information, such as user location, time of day and current activity. However, determining the users’ context throughout their daily activities is one of the main challenges of context-aware computing. With the increasing number of built-in mobile sensors and other input sources, existing context models do not effectively handle context information related to personal user context. The objective of this research was to develop an improved context-aware model to support the context awareness needs of mobile applications. An existing context-aware model was selected as the most complete model to use as a basis for the proposed model to support context awareness in mobile applications. The existing context-aware model was modified to address the shortcomings of existing models in dealing with context information related to personal user context. The proposed model supports four different context dimensions, namely Physical, User Activity, Health and User Preferences. A prototype, called CoPro was developed, based on the proposed model, to demonstrate the effectiveness of the model. Several experiments were designed and conducted to determine if CoPro was effective, reliable and capable. CoPro was considered effective as it produced low-level context as well as inferred context. The reliability of the model was confirmed by evaluating CoPro using Quality of Context (QoC) metrics such as Accuracy, Freshness, Certainty and Completeness. CoPro was also found to be capable of dealing with the limitations of the mobile computing platform such as limited processing power. The research determined that the proposed context-aware model can be used to successfully support context awareness in mobile applications. Design recommendations were proposed and future work will involve converting the CoPro prototype into middleware in the form of an API to provide easier access to context awareness support in mobile applications.

Online information access on mobile devices is increasing as a result of the growth in the use of Internet-enabled handheld (or pocket-size) devices. The combined influence of recent enabling technologies such as Web 2.0, mobile app stores and improved wireless networks have driven the increase in online applications that allow users to access various types of information on mobile devices regardless of time and location. Examples of such applications (usually shortened to app) include: social media, such as FacebookTM App and TwitterTM App, banking applications such as (Standard Bank South Africa)TM Mobile Banking App and First National Bank (FNB) BankingTM App, and news application such as news 24TM App and BBCTM News App. Online businesses involved in buying, selling and business transaction processing activities via the Internet have exploited the opportunity to extend electronic commerce (e-commerce) initiatives into mobile commerce (m-commerce). Online businesses that interact with end user customers implement business to consumer (B2C) m-commerce applications that enable customers to access and browse product catalogue information on mobile devices, anytime, anywhere. Customers accessing electronic product catalogue information on a mobile device face a number of challenges such as a long list of products presented on a small screen and a longer information download time. These challenges mainly originate from the limiting and dynamic nature of the mobile apps operating environment, for example, dynamic location, bandwidth fluctuations and, diverse and limited device features, collectively referred to as context. The goal of this research was to design and implement a context-aware model that can be incorporated into an m-commerce application in order to improve the presentation of product catalogue information on m-commerce storefronts. The motivation for selecting product catalogue is prompted by literature which indicates that improved presentation of information in m-commerce (and e-commerce) applications has a positive impact on usability of the websites. Usable m-commerce (and e-commerce) websites improve efficiency in consumer behaviour that impacts sales, profits and business growth. The context-aware model aimed at collecting context information within the user environment and utilising it to determine optimal retrieval and presentation of product catalogue in m-commerce. An integrated logical context sensor and Mathematical algorithms were implemented in the context-aware model. The integrated logical context sensor was responsible for the collection of different types of predetermined context information such as device specification or capabilities, connection bandwidth, location and time of the day as well as the user profile. The algorithms transformed the collected context information into usable formats and enabled optimal retrieval and presentation of product catalogue data on a specific mobile device. Open-source implementation tools were utilised to implement components of the model including: HTML5, PhP, JavaScript and MySQL database. The context-aware model was incorporated into an existing m-commerce application. Two user evaluation studies were conducted during the course of the research. The first evaluation was to evaluate the accuracy of information collected by the context sensor component of the model. This survey was conducted with a sample of 30 users from different countries across the world. In-between the context sensor and main evaluation surveys, a pilot study was conducted with a sample of 19 users with great experience in mobile application development and use from SAP Next Business and Technology, Africa. Finally an overall user evaluation study was conducted with a sample of 30 users from a remote area called Kgautswane in Limpopo Province, South Africa. The results obtained indicate that the context-aware model was able to determine accurate context information in real-time and effectively determine how much product information should be retrieved and how the information should be presented on a mobile device interface. Two main contributions emerged from the research, first the research contributed to the field of mobile Human Computer Interaction. During the research, techniques of evaluating and improving usability of mobile applications were demonstrated. Secondly, the research made a significant contribution to the upcoming field of context-aware computing. The research brought clarity with regard to context-aware computing which is lacking in existing, current research despite the field’s proven impact of improving usability of applications. Researchers can utilise contributions made in this research to develop further techniques and usable context-aware solutions.