New Frameworks for Secure Image Communication in the Internet of Things (IoT)

The continuous expansion of technology, broadband connectivity and the wide range of new devices in the IoT cause serious concerns regarding privacy and security. In addition, in the IoT a key challenge is the storage and management of massive data streams. For example, there is always the demand for acceptable size with the highest quality possible for images to meet the rapidly increasing number of multimedia applications. The effort in this dissertation contributes to the resolution of concerns related to the security and compression functions in image communications in the Internet of Thing (IoT), due to the fast of evolution of IoT. This dissertation proposes frameworks for a secure digital camera in the IoT. The objectives of this dissertation are twofold. On the one hand, the proposed framework architecture offers a double-layer of protection: encryption and watermarking that will address all issues related to security, privacy, and digital rights management (DRM) by applying a hardware architecture of the state-of-the-art image compression technique Better Portable Graphics (BPG), which achieves high compression ratio with small size. On the other hand, the proposed framework of SBPG is integrated with the Digital Camera. Thus, the proposed framework of SBPG integrated with SDC is suitable for high performance imaging in the IoT, such as Intelligent Traffic Surveillance (ITS) and Telemedicine. Due to power consumption, which has become a major concern in any portable application, a low-power design of SBPG is proposed to achieve an energy- efficient SBPG design. As the visual quality of the watermarked and compressed images improves with larger values of PSNR, the results show that the proposed SBPG substantially increases the quality of the watermarked compressed images. Higher value of PSNR also shows how robust the algorithm is to different types of attack. From the results obtained for the energy- efficient SBPG design, it can be observed that the power consumption is substantially reduced, up to 19%

Adaptivity of 3D web content in web-based virtual museums : a quality of service and quality of experience perspective

The 3D Web emerged as an agglomeration of technologies that brought the third dimension to the World Wide Web. Its forms spanned from being systems with limited 3D capabilities to complete and complex Web-Based Virtual Worlds. The advent of the 3D Web provided great opportunities to museums by giving them an innovative medium to disseminate collections' information and associated interpretations in the form of digital artefacts, and virtual reconstructions thus leading to a new revolutionary way in cultural heritage curation, preservation and dissemination thereby reaching a wider audience. This audience consumes 3D Web material on a myriad of devices (mobile devices, tablets and personal computers) and network regimes (WiFi, 4G, 3G, etc.). Choreographing and presenting 3D Web components across all these heterogeneous platforms and network regimes present a significant challenge yet to overcome. The challenge is to achieve a good user Quality of Experience (QoE) across all these platforms. This means that different levels of fidelity of media may be appropriate. Therefore, servers hosting those media types need to adapt to the capabilities of a wide range of networks and devices. To achieve this, the research contributes the design and implementation of Hannibal, an adaptive QoS & QoE-aware engine that allows Web-Based Virtual Museums to deliver the best possible user experience across those platforms. In order to ensure effective adaptivity of 3D content, this research furthers the understanding of the 3D web in terms of Quality of Service (QoS) through empirical investigations studying how 3D Web components perform and what are their bottlenecks and in terms of QoE studying the subjective perception of fidelity of 3D Digital Heritage artefacts. Results of these experiments lead to the design and implementation of Hannibal

Autocalibrating vision guided navigation of unmanned air vehicles via tactical monocular cameras in GPS denied environments

This thesis presents a novel robotic navigation strategy by using a conventional tactical monocular camera, proving the feasibility of using a monocular camera as the sole proximity sensing, object avoidance, mapping, and path-planning mechanism to fly and navigate small to medium scale unmanned rotary-wing aircraft in an autonomous manner. The range measurement strategy is scalable, self-calibrating, indoor-outdoor capable, and has been biologically inspired by the key adaptive mechanisms for depth perception and pattern recognition found in humans and intelligent animals (particularly bats), designed to assume operations in previously unknown, GPS-denied environments. It proposes novel electronics, aircraft, aircraft systems, systems, and procedures and algorithms that come together to form airborne systems which measure absolute ranges from a monocular camera via passive photometry, mimicking that of a human-pilot like judgement. The research is intended to bridge the gap between practical GPS coverage and precision localization and mapping problem in a small aircraft. In the context of this study, several robotic platforms, airborne and ground alike, have been developed, some of which have been integrated in real-life field trials, for experimental validation. Albeit the emphasis on miniature robotic aircraft this research has been tested and found compatible with tactical vests and helmets, and it can be used to augment the reliability of many other types of proximity sensors