With the popularity of diverse mobile devices, integrating existing systems or softwares of personal computers with different devices is an active research. Transcoding plays an important role in this issue. In the thesis, we develop a transcoding system that converts frames based on the device profile and user¡¦s preferences to present retrieved results of existing systems into image format. The proposed system allows a variety of mobile devices to efficiently display the retrieved results from existing systems. To overcome the weakness of the lack for interaction of existing transcoding systems, our proposed system supports interaction between clients and server.

The AECL National Research Universal (NRU) reactor has implemented a new mobile device to help Operators collect instrument data. Instruments display readings through various analog and digital devices. Before the change, Operators collected readings via a paper and pen method. With the implementation of the mobile device, Operators scan a barcode and input the instrument’s reading using a mobile device. To understand the factors that influence Operator acceptance of the new technology, the Technology Acceptance Model (TAM) was used to identify determinants that have the biggest impact on the Operators’ intention to use the mobile device. The TAM results identified the determinants that were most influential and the mobile interface was redesigned, based on the TAM results. Once the interfaces were redesigned they were evaluated through a usability study that compared the original interface with the newly designed screens. The results did not show statistically significant differences between the screens.

The AECL National Research Universal (NRU) reactor has implemented a new mobile device to help Operators collect instrument data. Instruments display readings through various analog and digital devices. Before the change, Operators collected readings via a paper and pen method. With the implementation of the mobile device, Operators scan a barcode and input the instrument’s reading using a mobile device. To understand the factors that influence Operator acceptance of the new technology, the Technology Acceptance Model (TAM) was used to identify determinants that have the biggest impact on the Operators’ intention to use the mobile device. The TAM results identified the determinants that were most influential and the mobile interface was redesigned, based on the TAM results. Once the interfaces were redesigned they were evaluated through a usability study that compared the original interface with the newly designed screens. The results did not show statistically significant differences between the screens.

Adoption of smart mobile devices (smartphones, wearables, etc.) is rapidly growing. There are already over 2 billion smartphone users worldwide [1] and the percentage of smartphone users is expected to be over 50% in the next five years [2]. These devices feature rich sensing capabilities which allow inferences about mobile device user’s surroundings and behavior. Multiple and diverse sensors common on such mobile devices facilitate observing the environment from different perspectives, which helps to increase robustness of inferences and enables more complex context detection tasks. Though a larger number of sensing modalities can be beneficial for more accurate and wider mobile context detection, integrating these sensor streams is non-trivial. This thesis presents how multimodal sensor data can be integrated to facilitate ro- bust and energy efficient mobile context detection, considering three important and challenging detection tasks: indoor localization, indoor-outdoor detection and human activity recognition. This thesis presents three methods for multimodal sensor inte- gration, each applied for a different type of context detection task considered in this thesis. These are gradually decreasing in design complexity, starting with a solution based on an engineering approach decomposing context detection to simpler tasks and integrating these with a particle filter for indoor localization. This is followed by man- ual extraction of features from different sensors and using an adaptive machine learn- ing technique called semi-supervised learning for indoor-outdoor detection. Finally, a method using deep neural networks capable of extracting non-intuitive features di- rectly from raw sensor data is used for human activity recognition; this method also provides higher degree of generalization to other context detection tasks. Energy efficiency is an important consideration in general for battery powered mo- bile devices and context detection is no exception. In the various context detection tasks and solutions presented in this thesis, particular attention is paid to this issue by relying largely on sensors that consume low energy and on lightweight computations. Overall, the solutions presented improve on the state of the art in terms of accuracy and robustness while keeping the energy consumption low, making them practical for use on mobile devices.

It is commonly acknowledged that mobile devices now form an integral part of an individual’s everyday life. The modern mobile handheld devices are capable to provide a wide range of services and applications over multiple networks. With the increasing capability and accessibility, they introduce additional demands in term of security. This thesis explores the need for authentication on mobile devices and proposes a novel mechanism to improve the current techniques. The research begins with an intensive review of mobile technologies and the current security challenges that mobile devices experience to illustrate the imperative of authentication on mobile devices. The research then highlights the existing authentication mechanism and a wide range of weakness. To this end, biometric approaches are identified as an appropriate solution an opportunity for security to be maintained beyond point-of-entry. Indeed, by utilising behaviour biometric techniques, the authentication mechanism can be performed in a continuous and transparent fashion. This research investigated three behavioural biometric techniques based on SMS texting activities and messages, looking to apply these techniques as a multi-modal biometric authentication method for mobile devices. The results showed that linguistic profiling; keystroke dynamics and behaviour profiling can be used to discriminate users with overall Equal Error Rates (EER) 12.8%, 20.8% and 9.2% respectively. By using a combination of biometrics, the results showed clearly that the classification performance is better than using single biometric technique achieving EER 3.3%. Based on these findings, a novel architecture of multi-modal biometric authentication on mobile devices is proposed. The framework is able to provide a robust, continuous and transparent authentication in standalone and server-client modes regardless of mobile hardware configuration. The framework is able to continuously maintain the security status of the devices. With a high level of security status, users are permitted to access sensitive services and data. On the other hand, with the low level of security, users are required to re-authenticate before accessing sensitive service or data.

Abstract is available to download.

With the use of an immersive display system such as CAVE system, the user is able to realize a 3D immersive virtual environment realistically. However, interacting with virtual worlds in CAVE systems using traditional input devices to perform easy operations such as manipulation, object selection, and navigation is often difficult. This difficulty could diminish the immersion and sense of presence when it comes to 3D virtual environment tasks. Our research aims to implement and evaluate alternative approaches of interaction with immersive virtual environments on mobile devices for manipulation and object selection tasks. As many researchers have noted, using a mobile device as an interaction device has a number of advantages, including built-in display, built-in control, and touch screen facility. These advantages facilitate simple tasks within immersive virtual environments. This research proposes new methods using mobile devices like Smart-phones to perform di↵erent kinds of interactions both as an input device, (e.g. performing selection and manipulation of objects) and as an output device (e.g. utilizing the screen as an extra view for a virtual camera or information display). Moreover, we developed a prototype system to demonstrate and informally evaluate these methods. The research conclusion suggests using mobile devices as a 3D-controller. This will be a more intuitive approach to interact within the virtual environment.

With the continued proliferation of mobile devices, the collection of information associated with such devices and their users - such as location, installed applications and cookies associated with built-in browsers - has become increasingly straightforward. By analysing such information, organisations are often able to deliver more relevant and better focused advertisements. Although such Targeted Mobile Advertising (TMA) offers great benefits to advertisers, it gives rise to a number of concerns, with privacy-related concerns being prominent amongst them. It follows that there is a need for an advertisement-selection mechanism that can support the existing TMA business model in a manner that takes into account consumers' privacy concerns. The research described in this dissertation explores the delicate balance between the goals of the advertisers and the consumers: advertisers pursue profits by applying TMA, which violates consumers' privacy; consumers hope to benefit from useful mobile advertisements without compromising their personal information. The conflicts of interests between consumers and advertisers in the context of targeted mobile advertising brings us to our research question: Is it possible to develop a privacy-preserving TMA framework that enables mobile users to take advantage of useful advertising services without their privacy being compromised, and without impacting significantly advertising effectiveness? In order to answer this question, this dissertation presents four main contributions. First, we report upon the result of a qualitative study to discuss the balance that needs to be struck between privacy and utility in this emerging area. Second, a number of formal models are developed to reason about privacy, as well as to reason about the relationship between privacy and utility in the context of TMA. Third, a novel ad-selection architecture, PPTMA (Privacy-Preserving Targeted Mobile Advertising), is presented and evaluated. Finally, a privacy-preserving advertisement-selection mechanism, AdSelector, is introduced. The mechanism is novel in its combination of a user subscription mechanism, a two-stage ad-selection process, and the application of a trustworthy billing system.

A Denial of Service (DoS) attack is an incident in which the user is deprived of the services of a resource he is expected to have. With the increasing reliance on mobile devices like laptops and palmtops, a new type of DoS attack is possible that attacks the batteries of these devices, called "sleep deprivation attacks". The goal of sleep deprivation attacks is to rapidly drain the battery of the mobile devices, rendering the device inoperable long before the expected battery lifetime, thus denying the service the user expects from the mobile device. The purpose of this research is to investigate these types of attacks so that proper defense mechanisms can be put in place before the attacks become a more sophisticated and potent force. This research presents three different possible methods that can be adopted by an attacker to drain the battery of a device i.e. malignant attacks, benign attacks and network service request attacks. These attacks are implemented on a variety of mobile computing platforms like palmtops and a laptop and the corresponding results are presented. Finally, a mathematical model is presented that estimates the battery life of a device based on its power consumption in various power management states and expected usage. This model can also be used to predict the impact of a DoS attack on the battery life of the device under attack. / Master of Science

Philosophiae Doctor - PhD / The global use of mobile devices, and their connectivity capacity, integrated with the affordances of social media networks, provides a resource-rich platform for innovative student-directed learning experiences. Technology has become embedded in the daily lives of students, who become more approachable when technology is used within the higher education context. In 2014 the Educause Centre for Analysis and Research partnered with 213 higher education institutions across the United States of America. It was established that 86 percent of undergraduate information technology students owned a smartphone and half of that percentage owned a tablet. A systematic review on mobile learning in higher education focusing on the African Perspective in 2017 concluded that there was an increase in the use of mobile learning in higher education. Higher education institutions continue to move away from traditional, lecture-based lessons towards new, innovative teaching and learning methodologies to facilitate emerging pedagogies and strategies, thereby enhancing student learning. The adoption of technological innovation could promote the unfolding of a social process that over time could enhance social connectedness among young students and their older adult educators. Mobile learning is fundamentally defined as “learning with mobile devices” and it has the potential to extend the philosophies of learning through innovation It was identified that research in the field of m-learning can be divided into four areas, namely: pedagogy; administrative issues and technological challenges; ensuring sustainable development in education using m-learning; and the impact of new applications. With the increased need for nursing professionals, promoting the quality and effectiveness of nursing education has become crucial. It is thus important to establish learning environments in which personalised guidance and feedback to students regarding their practical skills and the application of their theoretical knowledge within clinical learning environments is provided.