Measuring QoS in an aeronautical opportunistic network architecture with limited access to a satellite communications backhaul

We measure quality of service (QoS) in a wireless network architecture of transoceanic aircraft. A distinguishing characteristic of the network scheme we analyze is that it mixes the concept of Delay Tolerant Networking (DTN) through the exploitation of opportunistic contacts, together with direct satellite access in a limited number of the nodes. We provide a graph sparsification technique for deriving a network model that satisfies the key properties of a real aeronautical opportunistic network while enabling scalable simulation. This reduced model allows us to analyze the impact regarding QoS of introducing Internet-like traffic in the form of outgoing data from passengers. Promoting QoS in DTNs is usually really challenging due to their long delays and scarce resources. The availability of satellite communication links offers a chance to provide an improved degree of service regarding a pure opportunistic approach, and therefore it needs to be properly measured and quantified. Our analysis focuses on several QoS indicators such as delivery time, delivery ratio, and bandwidth allocation fairness. Obtained results show significant improvements in all metric indicators regarding QoS, not usually achievable on the field of DTNs.

MyRoom: A user-centred model of affective responsive architecture

Can my immediate physical environment affect how I feel? The instinctive answer to this question must be a resounding “yes”. What might seem a throwaway remark is increasingly borne out by research in environmental and behavioural psychology, and in the more recent discipline of Evidence-Based Design. Research outcomes are beginning to converge with findings in neuroscience and neurophysiology, as we discover more about how the human brain and body functions, and reacts to environmental stimuli. What we see, hear, touch, and sense affects each of us psychologically and, by extension, physically, on a continual basis. The physical characteristics of our daily environment thus have the capacity to profoundly affect all aspects of our functioning, from biological systems to cognitive ability. This has long been understood on an intuitive basis, and utilised on a more conscious basis by architects and other designers. Recent research in evidence-based design, coupled with advances in neurophysiology, confirm what have been previously held as commonalities, but also illuminate an almost frightening potential to do enormous good, or alternatively, terrible harm, by virtue of how we make our everyday surroundings. The thesis adopts a design methodology in its approach to exploring the potential use of wireless sensor networks in environments for elderly people. Vitruvian principles of “commodity, firmness and delight” inform the research process and become embedded in the final design proposals and research conclusions. The issue of person-environment fit becomes a key principle in describing a model of continuously-evolving responsive architecture which makes the individual user its focus, with the intention of promoting wellbeing. The key research questions are: What are the key system characteristics of an adaptive therapeutic single-room environment? How can embedded technologies be utilised to maximise the adaptive and therapeutic aspects of the personal life-space of an elderly person with dementia?.

The architecture of influence: Paul Rudolph's Art and Architecture Building at Yale

Focussing on Paul Rudolph’s Art & Architecture Building at Yale, this thesis demonstrates how the building synthesises the architect’s attitude to architectural education, urbanism and materiality. It tracks the evolution of the building from its origins – which bear a relationship to Rudolph’s pedagogical ideas – to later moments when its occupants and others reacted to it in a series of ways that could never have been foreseen. The A&A became the epicentre of the university’s counter culture movement before it was ravaged by a fire of undetermined origins. Arguably, it represents the last of its kind in American architecture, a turning point at the threshold of postmodernism. Using an archive that was only made available to researchers in 2009, this is the first study to draw extensively on the research files of the late architectural writer and educator, C. Ray Smith. Smith’s 1981 manuscript about the A&A entitled “The Biography of a Building,” was never published. The associated research files and transcripts of discussions with some thirty interviewees, including Rudolph, provide a previously unavailable wealth of information. Following Smith’s methodology, meetings were recorded with those involved in the A&A including, where possible, some of Smith’s original interviewees. When placed within other significant contexts – the physicality of the building itself as well as the literature which surrounds it – these previously untold accounts provide new perspectives and details, which deepen the understanding of the building and its place within architectural discourse. Issues revealed include the importance of the influence of Louis Kahn’s Yale Art Gallery and Yale’s Collegiate Gothic Campus on the building’s design. Following a tumultuous first fifty years, the A&A remains an integral part of the architectural education of Yale students and, furthermore, constitutes an important didactic tool for all students of architecture.

Efficient delivery of scalable media streaming over lossy networks

Recent years have witnessed a rapid growth in the demand for streaming video over the Internet, exposing challenges in coping with heterogeneous device capabilities and varying network throughput. When we couple this rise in streaming with the growing number of portable devices (smart phones, tablets, laptops) we see an ever-increasing demand for high-definition videos online while on the move. Wireless networks are inherently characterised by restricted shared bandwidth and relatively high error loss rates, thus presenting a challenge for the efficient delivery of high quality video. Additionally, mobile devices can support/demand a range of video resolutions and qualities. This demand for mobile streaming highlights the need for adaptive video streaming schemes that can adjust to available bandwidth and heterogeneity, and can provide us with graceful changes in video quality, all while respecting our viewing satisfaction. In this context the use of well-known scalable media streaming techniques, commonly known as scalable coding, is an attractive solution and the focus of this thesis. In this thesis we investigate the transmission of existing scalable video models over a lossy network and determine how the variation in viewable quality is affected by packet loss. This work focuses on leveraging the benefits of scalable media, while reducing the effects of data loss on achievable video quality. The overall approach is focused on the strategic packetisation of the underlying scalable video and how to best utilise error resiliency to maximise viewable quality. In particular, we examine the manner in which scalable video is packetised for transmission over lossy networks and propose new techniques that reduce the impact of packet loss on scalable video by selectively choosing how to packetise the data and which data to transmit. We also exploit redundancy techniques, such as error resiliency, to enhance the stream quality by ensuring a smooth play-out with fewer changes in achievable video quality. The contributions of this thesis are in the creation of new segmentation and encapsulation techniques which increase the viewable quality of existing scalable models by fragmenting and re-allocating the video sub-streams based on user requirements, available bandwidth and variations in loss rates. We offer new packetisation techniques which reduce the effects of packet loss on viewable quality by leveraging the increase in the number of frames per group of pictures (GOP) and by providing equality of data in every packet transmitted per GOP. These provide novel mechanisms for packetizing and error resiliency, as well as providing new applications for existing techniques such as Interleaving and Priority Encoded Transmission. We also introduce three new scalable coding models, which offer a balance between transmission cost and the consistency of viewable quality.

Context-aware real-time assistant architecture for pervasive healthcare

The aging population in many countries brings into focus rising healthcare costs and pressure on conventional healthcare services. Pervasive healthcare has emerged as a viable solution capable of providing a technology-driven approach to alleviate such problems by allowing healthcare to move from the hospital-centred care to self-care, mobile care, and at-home care. The state-of-the-art studies in this field, however, lack a systematic approach for providing comprehensive pervasive healthcare solutions from data collection to data interpretation and from data analysis to data delivery. In this thesis we introduce a Context-aware Real-time Assistant (CARA) architecture that integrates novel approaches with state-of-the-art technology solutions to provide a full-scale pervasive healthcare solution with the emphasis on context awareness to help maintaining the well-being of elderly people. CARA collects information about and around the individual in a home environment, and enables accurately recognition and continuously monitoring activities of daily living. It employs an innovative reasoning engine to provide accurate real-time interpretation of the context and current situation assessment. Being mindful of the use of the system for sensitive personal applications, CARA includes several mechanisms to make the sophisticated intelligent components as transparent and accountable as possible, it also includes a novel cloud-based component for more effective data analysis. To deliver the automated real-time services, CARA supports interactive video and medical sensor based remote consultation. Our proposal has been validated in three application domains that are rich in pervasive contexts and real-time scenarios: (i) Mobile-based Activity Recognition, (ii) Intelligent Healthcare Decision Support Systems and (iii) Home-based Remote Monitoring Systems.

A distributed architecture for the monitoring and analysis of time series data

It is estimated that the quantity of digital data being transferred, processed or stored at any one time currently stands at 4.4 zettabytes (4.4 × 2 70 bytes) and this figure is expected to have grown by a factor of 10 to 44 zettabytes by 2020. Exploiting this data is, and will remain, a significant challenge. At present there is the capacity to store 33% of digital data in existence at any one time; by 2020 this capacity is expected to fall to 15%. These statistics suggest that, in the era of Big Data, the identification of important, exploitable data will need to be done in a timely manner. Systems for the monitoring and analysis of data, e.g. stock markets, smart grids and sensor networks, can be made up of massive numbers of individual components. These components can be geographically distributed yet may interact with one another via continuous data streams, which in turn may affect the state of the sender or receiver. This introduces a dynamic causality, which further complicates the overall system by introducing a temporal constraint that is difficult to accommodate. Practical approaches to realising the system described above have led to a multiplicity of analysis techniques, each of which concentrates on specific characteristics of the system being analysed and treats these characteristics as the dominant component affecting the results being sought. The multiplicity of analysis techniques introduces another layer of heterogeneity, that is heterogeneity of approach, partitioning the field to the extent that results from one domain are difficult to exploit in another. The question is asked can a generic solution for the monitoring and analysis of data that: accommodates temporal constraints; bridges the gap between expert knowledge and raw data; and enables data to be effectively interpreted and exploited in a transparent manner, be identified? The approach proposed in this dissertation acquires, analyses and processes data in a manner that is free of the constraints of any particular analysis technique, while at the same time facilitating these techniques where appropriate. Constraints are applied by defining a workflow based on the production, interpretation and consumption of data. This supports the application of different analysis techniques on the same raw data without the danger of incorporating hidden bias that may exist. To illustrate and to realise this approach a software platform has been created that allows for the transparent analysis of data, combining analysis techniques with a maintainable record of provenance so that independent third party analysis can be applied to verify any derived conclusions. In order to demonstrate these concepts, a complex real world example involving the near real-time capturing and analysis of neurophysiological data from a neonatal intensive care unit (NICU) was chosen. A system was engineered to gather raw data, analyse that data using different analysis techniques, uncover information, incorporate that information into the system and curate the evolution of the discovered knowledge. The application domain was chosen for three reasons: firstly because it is complex and no comprehensive solution exists; secondly, it requires tight interaction with domain experts, thus requiring the handling of subjective knowledge and inference; and thirdly, given the dearth of neurophysiologists, there is a real world need to provide a solution for this domain

SDC: Scalable description coding for adaptive streaming media

Video compression techniques enable adaptive media streaming over heterogeneous links to end-devices. Scalable Video Coding (SVC) and Multiple Description Coding (MDC) represent well-known techniques for video compression with distinct characteristics in terms of bandwidth efficiency and resiliency to packet loss. In this paper, we present Scalable Description Coding (SDC), a technique to compromise the tradeoff between bandwidth efficiency and error resiliency without sacrificing user-perceived quality. Additionally, we propose a scheme that combines network coding and SDC to further improve the error resiliency. SDC yields upwards of 25% bandwidth savings over MDC. Additionally, our scheme features higher quality for longer durations even at high packet loss rates.

ALD: adaptive layer distribution for scalable video

Recent years have witnessed a rapid growth in the demand for streaming video over the Internet and mobile networks, exposes challenges in coping with heterogeneous devices and varying network throughput. Adaptive schemes, such as scalable video coding, are an attractive solution but fare badly in the presence of packet losses. Techniques that use description-based streaming models, such as multiple description coding (MDC), are more suitable for lossy networks, and can mitigate the effects of packet loss by increasing the error resilience of the encoded stream, but with an increased transmission byte cost. In this paper, we present our adaptive scalable streaming technique adaptive layer distribution (ALD). ALD is a novel scalable media delivery technique that optimises the tradeoff between streaming bandwidth and error resiliency. ALD is based on the principle of layer distribution, in which the critical stream data are spread amongst all packets, thus lessening the impact on quality due to network losses. Additionally, ALD provides a parameterised mechanism for dynamic adaptation of the resiliency of the scalable video. The Subjective testing results illustrate that our techniques and models were able to provide levels of consistent high-quality viewing, with lower transmission cost, relative to MDC, irrespective of clip type. This highlights the benefits of selective packetisation in addition to intuitive encoding and transmission.

ALD: adaptive layer distribution for scalable video

Bandwidth constriction and datagram loss are prominent issues that affect the perceived quality of streaming video over lossy networks, such as wireless. The use of layered video coding seems attractive as a means to alleviate these issues, but its adoption has been held back in large part by the inherent priority assigned to the critical lower layers and the consequences for quality that result from their loss. The proposed use of forward error correction (FEC) as a solution only further burdens the bandwidth availability and can negate the perceived benefits of increased stream quality. In this paper, we propose Adaptive Layer Distribution (ALD) as a novel scalable media delivery technique that optimises the tradeoff between the streaming bandwidth and error resiliency. ALD is based on the principle of layer distribution, in which the critical stream data is spread amongst all datagrams thus lessening the impact on quality due to network losses. Additionally, ALD provides a parameterised mechanism for dynamic adaptation of the scalable video, while providing increased resilience to the highest quality layers. Our experimental results show that ALD improves the perceived quality and also reduces the bandwidth demand by up to 36% in comparison to the well-known Multiple Description Coding (MDC) technique.