NETCODE: an XOR-based warning dissemination scheme for vehicular wireless networks

The next generation of vehicles will be equipped with automated Accident Warning Systems (AWSs) capable of warning neighbouring vehicles about hazards that might lead to accidents. The key enabling technology for these systems is the Vehicular Ad-hoc Networks (VANET) but the dynamics of such networks make the crucial timely delivery of warning messages challenging. While most previously attempted implementations have used broadcast-based data dissemination schemes, these do not cope well as data traffic load or network density increases. This problem of sending warning messages in a timely manner is addressed by employing a network coding technique in this thesis. The proposed NETwork COded DissEmination (NETCODE) is a VANET-based AWS responsible for generating and sending warnings to the vehicles on the road. NETCODE offers an XOR-based data dissemination scheme that sends multiple warning in a single transmission and therefore, reduces the total number of transmissions required to send the same number of warnings that broadcast schemes send. Hence, it reduces contention and collisions in the network improving the delivery time of the warnings. The first part of this research (Chapters 3 and 4) asserts that in order to build a warning system, it is needful to ascertain the system requirements, information to be exchanged, and protocols best suited for communication between vehicles. Therefore, a study of these factors along with a review of existing proposals identifying their strength and weakness is carried out. Then an analysis of existing broadcast-based warning is conducted which concludes that although this is the most straightforward scheme, loading can result an effective collapse, resulting in unacceptably long transmission delays. The second part of this research (Chapter 5) proposes the NETCODE design, including the main contribution of this thesis, a pair of encoding and decoding algorithms that makes the use of an XOR-based technique to reduce transmission overheads and thus allows warnings to get delivered in time. The final part of this research (Chapters 6--8) evaluates the performance of the proposed scheme as to how it reduces the number of transmissions in the network in response to growing data traffic load and network density and investigates its capacity to detect potential accidents. The evaluations use a custom-built simulator to model real-world scenarios such as city areas, junctions, roundabouts, motorways and so on. The study shows that the reduction in the number of transmissions helps reduce competition in the network significantly and this allows vehicles to deliver warning messages more rapidly to their neighbours. It also examines the relative performance of NETCODE when handling both sudden event-driven and longer-term periodic messages in diverse scenarios under stress caused by increasing numbers of vehicles and transmissions per vehicle. This work confirms the thesis' primary contention that XOR-based network coding provides a potential solution on which a more efficient AWS data dissemination scheme can be built.

Congenital thoracic vertebral malformations in brachycephalic “screw-tailed” dog breeds: Validation of a human classification scheme and a method of Cobb angle measurement in the assessment of vertebral column deformity

Congenital vertebral malformations are common in brachycephalic “screw-tailed” dog breeds such as French bulldogs, English bulldogs, Boston terriers, and Pugs. Those vertebral malformations disrupt the normal vertebral column anatomy and biomechanics, potentially leading to deformity of the vertebral column and subsequent neurological dysfunction. The initial aim of this work was to study and determine whether the congenital vertebral malformations identified in those breeds could be translated in a radiographic classification scheme used in humans to give an improved classification, with clear and well-defined terminology, with the expectation that this would facilitate future study and clinical management in the veterinary field. Therefore, two observers who were blinded to the neurologic status of the dogs classified each vertebral malformation based on the human classification scheme of McMaster and were able to translate them successfully into a new classification scheme for veterinary use. The following aim was to assess the nature and the impact of vertebral column deformity engendered by those congenital vertebral malformations in the target breeds. As no gold standard exists in veterinary medicine for the calculation of the degree of deformity, it was elected to adapt the human equivalent, termed the Cobb angle, as a potential standard reference tool for use in veterinary practice. For the validation of the Cobb angle measurement method, a computerised semi-automatic technique was used and assessed by multiple independent observers. They observed not only that Kyphosis was the most common vertebral column deformity but also that patients with such deformity were found to be more likely to suffer from neurological deficits, more especially if their Cobb angle was above 35 degrees.