Traditional marine monitoring systems such as oceanographic and hydrographic re- search vessels use either wireless sensor networks with a limited coverage, or expensive satellite communication that is not suitable for small and mid-sized vessels. This the- sis proposes an Internet of Marine Things data acquisition and cartography system in the marine environment using Very High Frequency (VHF) available on the majority of ships. The proposed system is equipped with sensors such as sea depth, tempera- ture, wind speed and direction, and the collected data is sent through a Ship Ad-hoc Network (SANET) to 5G edge clouds connected to sink/base station nodes on shore. The sensory data is ultimately aggregated at a central cloud on the internet to produce up to date cartography systems. Several observations and challenges unique to the marine environment have been discussed and feed into the solutions presented. We have investigated the application of appropriate data quantization and compression techniques to the marine sensor data collected in order to reduce the size of transmit- ted data and achieve better transmission efficiency. The impact of marine sparsity on the network is examined and a marine Mobile Ad-hoc/Delay Tolerant hybrid routing protocol (MADNET) is proposed to switch automatically between Mobile Ad-hoc Network (MANET) and Delay Tolerant Network (DTN) routing according to the network connectivity. The low rate data transmission offered by VHF radio has been investigated in terms of the network bottlenecks and the data collection rate achiev- able near the sinks. A sensory data management and transmission approach has also been proposed at the 5G network core using Information Centric Networks (ICN) aimed at providing efficient and duplicate less transmission of marine sensory read- ings from the base station/sink nodes towards the central cloud. Therefore, SANETs are realized as part of a 5G infrastructure for marine environment monitoring, paving the way to the Internet of Marine Things (IoMaT).

Advancements in computing, electronics and mechanical systems have resulted in the creation of a new class of vehicles called autonomous vehicles. These vehicles function using sensory input with an on-board computation system. Self-driving vehicles use an ad hoc vehicular network called VANET. The network has ad hoc infrastructure with mobile vehicles that communicate through open wireless channels. This thesis studies the design and implementation of a novel intelligent intrusion detection system which secures the external communication of self-driving vehicles. This thesis makes the following four contributions: It proposes a hybrid intrusion detection system to protect the external communication in self-driving vehicles from potential attacks. This has been achieved using fuzzification and artificial intelligence. The second contribution is the incorporation of the Integrated Circuit Metrics (ICMetrics) for improved security and privacy. By using the ICMetrics, specific device features have been used to create a unique identity for vehicles. Our work is based on using the bias in on board sensory systems to create ICMetrics for self-driving vehicles. The incorporation of fuzzy petri net in autonomous vehicles is the third contribution of the thesis. Simulation results show that the scheme can successfully detect denial-of-service attacks. The design of a clustering based hierarchical detection system has also been presented to detect worm hole and Sybil attacks. The final contribution of this research is an integrated intrusion detection system which detects various attacks by using a central database in BusNet. The proposed schemes have been simulated using the data extracted from trace files. Simulation results have been compared and studied for high levels of detection capability and performance. Analysis shows that the proposed schemes provide high detection rate with a low rate of false alarm. The system can detect various attacks in an optimised way owing to a reduction in the number of features, fuzzification.

Objective. The main goal of this research is proposing a novel method of onset detection for Self-Paced (SP) Brain-Computer Interfaces (BCIs) to increase usability and practicality of BCIs towards real-world uses from laboratory research settings. Approach. To achieve this goal, various Sound-Production Related Cognitive Tasks (SPRCTs) were tested against idle state in offline and simulated-online experiments. An online experiment was then conducted that turned a messenger dialogue on when a new message arrived by executing the Sound Imagery (SI) onset detection task in real-life scenarios (e.g. watching video, reading text). The SI task was chosen as an onset task because of its advantages over other tasks: 1) Intuitiveness. 2) Beneficial for people with motor disabilities. 3) No significant overlap with other common, spontaneous cognitive states becoming easier to use in daily-life situations. 4) No dependence on user’s mother language. Main results. The final online experimental results showed the new SI onset task had significantly better performance than the Motor Imagery (MI) approach. 84.04% (SI) vs 66.79% (MI) TFP score for sliding image scenario, 80.84% vs 61.07% for watching video task. Furthermore, the onset response speed showed the SI task being significantly faster than MI. In terms of usability, 75% of subjects answered SI was easier to use. Significance. The new SPRCT outperforms typical MI for SP onset detection BCIs (significantly better performance, faster onset response and easier usability), therefore it would be more easily used in daily-life situations. Another contribution of this thesis is a novel EMG artefact-contaminated EEG channel selection and handling method that showed significant class separation improvement against typical blind source separation techniques. A new performance evaluation metric for SP BCIs, called true-false positive score was also proposed as a standardised performance assessment method that considers idle period length, which was not considered in other typical metrics.

In today modern life smart electrical devices are used to make the human lives more comfortable. Actually, this is the combination of electronics and communications that provides the opportunity for real time communication while the measured electricity by smart meters is sent to the energy provider. In this way smart meters in residential areas play an important role for two way interaction between several users and energy provider. Solving an optimization problem with regard to consideration of satisfaction of both sides of users and energy providers tends to achieve the optimum price that is sent to the users to optimize their consumption in peak demand periods that is the main goal of demand response management programs. As nowadays the renewable energy plays an important role in providing the request of the users specially in residential areas consideration of the concept of uncertainty is an important issue that is considered in this thesis. Therefore, solving the optimization problem in presence of load uncertainty is important topic that is investigated. Another interesting issue is consideration of users' number variation in presence of load uncertainty in dynamic pricing demand response programs which gives the advantage of having good estimation of optimum consumption level of users according to the optimum announced price. In this thesis these issues are considered for solving an Income Based and Utility Base optimization problems that are further explained in upcoming chapters. In chapter III ,which provides the first contribution of the thesis a novel algorithm called Income Based Optimization (IBO) is defined and compared with previously proposed Utility Based Optimization problem (UBO). The price, users' consumption versus provided energy capacity by energy provider in 24 hours period are simulated and analyzed. The effect of variation in other parameters dependent to the cost imposed to the energy provider and the parameters that affect the users level of satisfaction is also evaluated. In Chapter IV, existence of load uncertainty is considered in proposed UBO algorithm when it is assumed that number of users in each time slot is varying based on different distributions such as Uniform or Poison. The results for the average gap between energy provider's generating capacity and consumption of the users are compared with when number of users kept constant in presence of load uncertainty in 24 hours period. Moreover, the effect of different distributions on the gap between generating capacity and the users consumption is evaluated assuming the number of users are increasing and following the distributions. The results for the announced price in 24 hours period is also evaluated and further is extended to the average announced price with respect to increase in number of users when it is assumed that user entry and departure type is varying based on different distributions and the load uncertainty also is existed. In chapter V, the proposed IBO algorithm in chapter three is further extended to the Uncertain IBO and is called UIBO. Therefore, it is assumed that bounded uncertainty is added to the users consumption. This algorithm is further extended in a way that variation in number of users is considered based on different distributions. The results are evaluated for the average gap between generating capacity and users consumption in 24 hours period and is further extended with respect to consideration of the increasing pattern for the number of users in presence of load uncertainty and different types of distributions for the users number variation. With respect to consideration of UIBO algorithm the price in 24 hours period is evaluated and the results are further extended to evaluate the average price with respect to increasing pattern for number of users that are varying based on different distributions when the bounded uncertainty is added to the users consumption. Moreover, the achieved gain of the proposed algorithm based on the ratio of the variation of the announced price to the varying number of users is evaluated. Finally chapter VI provides the conclusion and suggestion for future work.

Cloud computing provides beneficial services to users, enabling them to share large amounts of information, employ Storage Nodes (SN), utilise Computing Nodes (CN) and gather knowledge for research. Virtual Machines (VMs) usually host data-intensive applications, which submit thousands of jobs that access subsets of the petabytes of data distributed over Clouds Datacentres (DCs). The VMs scheduling allocation decisions in cloud environments are based on different parameters, such as cost, resource utilisation, performance, time and resource availability. In the case of application performance, the decisions are often made on the basis of jobs being either data intensive or computation intensive. In data-intensive situations, jobs may be pushed to the data; in computation intensive situations, data may be pulled to the jobs. This kind of scheduling, in which there is no consideration of network characteristics, can lead to performance degradation in a cloud environment and may result in large processing queues and job execution delays due to site overloads. This thesis proposes a novel service framework, the network- aware VM placement approach for data- intensive applications (NADI), to address the need for improved application performance . NADI takes into account a jobs time cost based on a mechanism that maps VMs against the resources when making scheduling decisions across multiple DCs. So, it not only allocates the best available resources to a VM to minimise the time needed to complete its jobs but also checks the global state of jobs and resources so that the output of the whole cloud is maximised. The thesis begins with a statement of the problem addressed and the objectives of the research. The methodology adopted for the research is described subsequently, and the outline of the thesis is presented. This is followed by a brief introduction highlighting the current approaches in VM placement and migration in cloud computing. Next, this thesis presents a framework for the proposed NADI with a description of its various components and enabling functionalities, which are required to realise this framework. Multi-objective strategies suitable for the problems in NADI are presented. Novel algorithms for managing applications and their data are proposed; they aim to improve each jobs performance and minimise the traffic between the application and its related data. The results indicate that there are considerable performance improvements and that the completion time is reduced by 25% to 51%, which can be gained by adopting the NADI scheduling approach.

This thesis is mainly concerned with semantic data modelling related to database design. The domain of this research is restricted to general data modelling and the discussion is carried out at the conceptual level. The thesis assesses a number of serious modelling shortcomings of the conventional data models and reviews several basic principles and mechanisms developed in current semantic data modelling research. Based on these findings, the thesis identifies the inadequacy of the conceptualization of data modelling and develops the two-view conceptualization of data modelling. The basic idea behind the two-view conceptualization is that the conceptual structure of the applications being modelled should be separated from its external data representation. A new semantic database model, SDBM, is designed based on the conceptualization. The model makes a clear separation between the conceptual structure and its external data representation. It offers a data type mechanism to deal with the data representation, a window mechanism to model the conceptual structure, and a transaction mechanism to provide database operations. One of the major extensions of the current semantic data models is that with SDBM the specialization relationship is just a special case of constraints that can be specified among SDBM windows. A formal syntax and informal semantics of SDBM are given in the thesis along with comparisons between SDBM and a closely-related semantic data model, Taxis. / Business, Sauder School of / Graduate

The problems of software development for microcomputers are studied through the design and implementation of a simple systems language, called E. E is a descendant of the BCPL family of systems programming languages. Differences between E and its predecessors arise from the design of E as a minimal language, and from the objective of enabling interactive tracing of E programs. A development system for E has been constructed which may operate in a microcomputer with 16K bytes of memory. As well, E programs may be developed using a cross compiler which runs on a large computer system. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Unknown

This thesis examines the semantic definition of a programming language by a form of attribute grammar for ease of understanding. The attributes are expressed in a simple macro language and when evaluated produce code for an abstract machine. Part of an actual definition is looked at and found to be too obscure to be useful. The reasons for the obscurity are identified and suggestions are made for eliminating them. / Science, Faculty of / Computer Science, Department of / Graduate

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