In recent years, Optical Wireless (OW) communication techniques have attracted substantial attention as a benefit of their abundant spectral resources in the optical domain, which is a potential solution for satisfying the ever-increasing demand for increased wireless capacity in the conventional Radio Frequency (RF) band. Motivated by the emerging techniques and applications of OW communication, the Institute of Electrical and Electronics Engineers (IEEE) had released the IEEE standard 802.15.7 for short-range optical wireless communications, which categorised the Physical layer (PHY) of the OW communication into three candidate-solutions according to their advantages in different applications and environments: 1) Physical-layer I (PHY I): Free Space Optical (FSO)communication employs high-intensity Light Emitting Diodes (LEDs) or Laser Diodes (LDs) as its transmitter. 2) Physical-layer II (PHY II) uses cost-effective, low-power directional white LEDs for the dual function of illumination and communication. 3) Physical III (PHY-III) relies on the so-called Colour-Shift Keying (CSK) modulation scheme for supporting high-rate communication. Our investigations can be classified into three major categories, namely Optical Orthogonal Frequency Division Multiplexing (OFDM) based Multiple-Input Multiple-Output (MIMO) techniques for FSO communications in the context of PHY I, video streaming in PHY-II and the analysis and design of CSK for PHY-III. To be more explicit, in Chapter 2 we first construct a novel ACO-OFDM based MIMO system and investigate its performance under various FSO turbulence channel conditions. However, MIMO systems require multiple optical chains, hence their power consumption and hardware costs become substantial. Hence, we introduced the concept of Aperture Selection (ApS) to mitigate these problems with the aid of a simple yet efficient ApS algorithm for assisting our ACO-OFDM based MIMO system. Since the channel conditions of indoor Visible Light Communication (VLC) environments are more benign than the FSO-channels of Chapter 2, directional white LEDs are used to create an “attocell” in Chapter 3. More specifically, we investigate video streaming in a multi-Mobile Terminals (MTs) indoor VLC system relying on Unity Frequency Reuse (UFR) as well as on Higher Frequency Reuse Factor based Transmission (HFRFT) and on Vectored Transmission (VT) schemes. We minimise the distortion of video streaming, while satisfying the rate constraints as well as optical constraints of all the MTs. In Chapter 4 we analyse the performance of CSK relying both on joint Maximum Likelihood (ML) Hard-Detection (HD), as well as on the the Maximum A posteriori (MAP) criterion-based Soft-Detection (SD) of CSK. Finally, we conceive both two- stage and three-stage concatenated iterative receivers capable of achieving a substantial iteration gain, leading to a vanishingly low BER.

Electrical resistivity tomography (ERT) is a geophysical technique for modelling the properties of the shallow subsurface. The technique provides a powerful tool for a volumetric representation of the spatial properties and spatio-temporal systems below the ground by indirectly measuring electrical properties. ERT has wide-reaching applications for imaging and monitoring in fields such as mineral exploration, infrastructure, and groundwater modelling. Developing tools that can perform predictions and analysis on the resistivity models with limited intervention will allow for ERT systems to be deployed remotely so that they might serve as an alert system, for example, in areas at risk of landslides, or groundwater contamination. However, the nature of indirect observation in ERT imaging means that there is a high degree of uncertainty in the resolved models, resulting from systematic artefacts that occur in inversion processes and from the fact that the underlying structures and processes cannot be directly observed. This thesis presents a number of developments in automating the analysis and prediction of directly and indirectly observed uncertain systems, both static and dynamic. Drawing from principles in both fuzzy logic and probability, particularly Bayesian statistics, the different representations of uncertainty are exploited and utilised to make meaningful estimates of properties and parameters in noisy systems. The key contributions of the research presented include the unique combination of fuzzy inference systems in a recursive Bayesian estimator to resolve systems under the influence of multiple uncertain dynamic processes. Furthermore, frameworks for robustly isolating features with quantified certainty and for automatically tracking tracer moments in hydrodynamic systems are proposed and applied to a number of real-world case studies.

Considering the multiobjective nature of real-world optimisation problems requiring a search for optimal trade-off solutions, many multiobjective metaheuristics have been proposed in the scientific literature. As observed in previous studies, different approaches show strengths on different problems. A research question would be how to combine the strengths of those multiple multiobjective approaches to obtain improved performance across a range of problems. Hyper-heuristics, which emerged as general-purposed search methods with reusable components, are one of the design philosophies to achieve that goal. Hyper heuristics perform search over the space of (meta)heuristics by either selecting an appropriate (meta)heuristic or generating a new one from given components. Hierarchically, the (meta)heuristics are called low-level (meta)heuristics since they work underneath the selection or generation strategies of the hyper-heuristics. This feature leads to increased generality level for search methods and enables multiobjective hyper-heuristics to be applied to a wide range of problem domains than a metaheuristic tailored for a particular problem domain. A crucial component in such hyper-heuristics is learning and hence, various online and offline learning mechanisms have been adopted within hyper-heuristics. In this thesis, the focus is online learning based selection hyper-heuristics for multiobjective optimisation. To gain insights of the behaviour and roles of online learning mechanisms played in selection hyper-heuristics, nine hyper-heuristics including online learning based, predefined sequence based and random choice based are applied to and analysed on an 'unseen' real-world problem, wind farm layout optimisation. The empirical results show that selection hyper-heuristics can indeed exploit the strengths of different MOEAs. Meanwhile, it also suggests two research directions: find a reasonable combination of low-level multiobjective evolutionary algorithms (MOEAs) for the selection hyper-heuristic framework to perform search on, and come up with a more effective online learning mechanism for the hyper-heuristic framework to exploit the strengths of different low-level MOEAs. Therefore, a critical review of different types of MOEAs is carried out in order to develop a better understanding of their nature, advantages and disadvantages. This review would lead to a more informed decision on the choice of the low-level metaheuristic set that selection hyper-heuristics can operate on. In addition, based on the investigation of hypervolume guided EAs, an improved version of such algorithm is proposed in this thesis, which later is used as one of the low-level MOEAs in the proposed selection hyper-heuristics. Following this, two learning automata based selection hyper-heuristics for multiobjective optimisation are proposed which select an appropriate metaheuristic to perform at a given time based on the information gathered during the search. Due to the complicated nature of multiobjective optimisation, the learning automata in the proposed hyper-heuristics is employed in a non-traditional way and novel components are also designed for making the best use of the learned information. The proposed hyper-heuristics are compared with a range of multiobjective approaches including a state-of-the-art online learning based selection hyper-heuristic on four problem domains including two mathematical benchmark functions and two real-world problems. The experimental results demonstrate the superior performance and generality of the proposed approach. To further challenge the proposed hyper-heuristics, different numbers and types of metaheuristics are incorporated as the low-level metaheuristics and combined with different acceptance strategies. The proposed learning automata based hyper-heuristics are the best-performed ones based on the performance indicator, hypervolume µ-norm.

The need for teachers of Elementary School children to learn to program or rather to understand the Computational Thinking behind programming has been accelerated in many countries by the mandated teaching of programming in the Elementary School context. Many steps have been taken in order to create awareness of this issue, such as the Computing At Schools initiative (CAS) which is established in the UK. CAS aims to support teaching in computing and connected fields in UK schools. Moreover, in the USA the Computer Science Teachers Association (CSTA) was established to meet the purpose of informing and advising about the current development of computational thinking and to investigate and disseminate teaching and learning resources related to computational thinking. In Singapore research has been conducted by the government agency Infocomm Development Authority of Singapore (IDA) whereby the major goal is to meet the needs in the ICT sector and ultimately to focus and inspire learners about programming. The research for this thesis involves the development of a training scheme for pre-service teachers that will introduce them to computational thinking through the use of the Flash Action Script Development environment. Flash Action Scripts - amongst several other tools - are used as a tool for creating interactive content and because Flash is one of the premiere tools used to create content for the internet; a tool programmed with Flash looks practically the same in every browser and on every operating system. Flash Action scripts use traditional coding skills but permit the user to see how each piece of code affects the running or execution of the program, allowing the user to have an instant visual understanding of what the code is doing. It is also widely available within university campuses. A major problem in promoting the teaching of programming and computational thinking to Elementary School teachers is that the majority of such teachers have no concept of how to program and naturally are not motivated to learn programming. Experienced teachers involved in the current study felt that programming was too complicated and thus it was hard to gain fluency in programming. Student teachers who had no previous experience in programming were, however, easier to get engaged in learning programming principles. Eighty percent of this group found Action Scripting a useful tool to understand basic programming and scripting. The need to teach programming will motivate most but to learn through a tool that can be seen to have intrinsic value in their role as teachers has a greater potential of success. This thesis defines the design and implementation of a tool to use the learning of Flash Action Scripting as a motivational mechanism for pre-service teachers. The intrinsic value to them is intended to be utilisation of the learned Action Scripting skills to produce their own teaching material. Initial results indicate an enhanced engagement and motivation to learn to program and improved confidence in doing so. As projected the pre-service teachers had a more positive attitude towards the potential of the learning tool but both they and the in-service teachers had improved attitudes and enthusiasm after the experiment. The results show that both pre-service and in-service teachers can be trained to be designers and producers of digital courseware in the previous absence of computational thinking skills and definitely they can acquire skills in computer programming such as Flash Action Scripts.

Monads and applicative functors are staple design patterns to handle effects in pure functional programming, especially in Haskell with its built-in syntactic support. Over the last decade, however, practical needs and theoretical research have given rise to generalisations of monads and applicative functors. Examples are graded, indexed and constrained monads. The problem with these generalisations is that no unified representation of standard and generalised monads or applicatives exists in theory or practice. As a result, in Haskell, each generalisation has its own representation and library of functions. Hence, interoperability among the different notions is hampered and code is duplicated. To solve the above issues, I first survey the three most wide-spread generalisations of monads and applicatives: their graded, indexed and constrained variations. I then examine two approaches to give them a unified representation in Haskell: polymonads and supermonads. Both approaches are embodied in plugins for the Haskell compiler GHC that address most of the identified concerns. Finally, I examine category theory and propose unifying categorical models that encompass the three discussed generalisations together with the standard notions of monad and applicative.

This thesis presents a detailed study about one of the Multiple Criteria Decision Making (MCDM) models, namely Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), based on fuzzy set theory (FST) by focusing on improving modelling uncertain information provided by a group of decision makers (DMs). An exploration of issues and limitations in current models of standard TOPSIS and fuzzy TOPSIS were made. Despite many variations of type-1 fuzzy TOPSIS (T1-TOPSIS) model, none of the studies explaining the details of the key stages of standard TOPSIS (non-fuzzy) and T1-TOPSIS are based on a step-wise procedure. A detailed study was conducted which involve the process of identifying the limitations of standard TOPSIS and T1-TOPSIS. Based on this, a novel contribution on the comparison between these two models in systematic stepwise procedure was given. This study successfully identified and discussed the limitations, issues and challenges which have not been investigated sufficiently in the context of T1-TOPSIS model. Based on this exploration, further investigation of multiple variants of the extension of the fuzzy TOPSIS model for solving the MCDM problem was made with the primary aim of detailing the steps involved. One challenge that has risen is that it is not straightforward to differentiate between the multiple variants of TOPSIS existing today. A systematic comparison was made between standard T1-TOPSIS model with the recently extended model to show the differences between both models and to provide context for their respective strengths and limitations both in the complexity of application and expressiveness of results. Based on the resulting comparison, the differences in the steps implemented by these two Fuzzy TOPSIS models were highlighted throughout the worked example. Furthermore, this task highlights the ability of both models to handle different levels of uncertainty. Following the exploration of issues and limitations of the current model, as well as a comparative study, a novel extension of type-2 fuzzy TOPSIS model is proposed in this thesis which suggests providing an interval-valued output to reflect the uncertainties and to model subjective information. The proposed model enables to uniquely captures input uncertainty (i.e., decision-makers' preferences) in the decision-making outputs and provide a direct mapping of uncertainty in the inputs to outputs. By keeping the output values in interval form, the proposed model reduces the loss of information and maximises the potential benefit of using Interval Type-2 Fuzzy Sets (IT2 FSs). To demonstrate the MCDM problems when a various level of uncertainty is introduced, a novel experimental method was proposed in this study. The primary aim is to explore the use of IT2 FSs in handling uncertainty based on the TOPSIS model. This experiment was conducted to show how the variation of uncertainty levels in the input affects the final outputs. An implementation of the proposed model to two different case studies was conducted to evaluate the proposed model. The proposed model for the first time generates an interval-valued output. As intervals can, for example, exhibit partial overlap, a novel extended measure is proposed to compare the resulting interval-valued output from various cases (i.e., overlapping and non-overlapping) of the interval with considering uncertainty.

The root system of a plant is responsible for supplying it with essential nutrients. The plant's ability to explore the surrounding soil is largely determined by its root system architecture (RSA), which varies with both genetic and environmental conditions. X-ray micro computed tomography (µCT) is a powerful tool allowing the non-invasive study of the root system architecture of plants grown in natural soil environments, providing both 3D descriptions of root architecture and the ability to make multiple measurements over a period of time. Once volumetric µCT data is acquired, the root system must first be segmented from the surrounding soil environment and then described. Automated and semi-automated software tools can be used to extract roots from µCT images, but current methods for the recovery of RSA traits from the resulting volumetric descriptions are somewhat limited. This thesis presents a novel tool (RooTh) which, given a segmented µCT image, skeletonises the root system and quantifies global and local root traits with minimal user interaction. The computationally inexpensive method used takes advantage of curve-fitting and active contours to find the optimal skeleton and thus evaluate root traits objectively. A small-scale experiment was conducted to validate and compare root traits extracted using the method presented here alongside other 2D imaging tools. The results show a good degree of correlation between the two methods.

The emerging technology of mobile devices and cloud computing has brought a new and efficient way for data to be collected, processed and stored by mobile users. With improved specifications of mobile devices and various mobile applications provided by cloud servers, mobile users can enjoy tremendous advantages to manage their daily life through those applications instantaneously, conveniently and productively. However, using such applications may lead to the exposure of user data to unauthorised access when the data is outsourced for processing and storing purposes. Furthermore, such a setting raises the privacy breach and security issue to mobile users. As a result, mobile users would be reluctant to accept those applications without any guarantee on the safety of their data. The recent breakthrough of Fully Homomorphic Encryption (FHE) has brought a new solution for data processing in a secure motion. Several variants and improvements on the existing methods have been developed due to efficiency problems. Experience of such problems has led us to explore two areas of studies, Mobile Sensing Systems (MSS) and Mobile Cloud Computing (MCC). In MSS, the functionality of smartphones has been extended to sense and aggregate surrounding data for processing by an Aggregation Server (AS) that may be operated by a Cloud Service Provider (CSP). On the other hand, MCC allows resource-constraint devices like smartphones to fully leverage services provided by powerful and massive servers of CSPs for data processing. To support the above two application scenarios, this thesis proposes two novel schemes: an Accountable Privacy-preserving Data Aggregation (APDA) scheme and a Lightweight Homomorphic Encryption (LHE) scheme. MSS is a kind of WSNs, which implements a data aggregation approach for saving the battery lifetime of mobile devices. Furthermore, such an approach could improve the security of the outsourced data by mixing the data prior to be transmitted to an AS, so as to prevent the collusion between mobile users and the AS (or its CSP). The exposure of users’ data to other mobile users leads to a privacy breach and existing methods on preserving users’ privacy only provide an integrity check on the aggregated data without being able to identify any misbehaved nodes once the integrity check has failed. Thus, to overcome such problems, our first scheme APDA is proposed to efficiently preserve privacy and support accountability of mobile users during the data aggregation. Furthermore, APDA is designed with three versions to provide balanced solutions in terms of misbehaved node detection and data aggregation efficiency for different application scenarios. In addition, the successfully aggregated data also needs to be accompanied by some summary information based on necessary additive and non-additive functions. To preserve the privacy of mobile users, such summary could be executed by implementing existing privacy-preserving data aggregation techniques. Nevertheless, those techniques have limitations in terms of applicability, efficiency and functionality. Thus, our APDA has been extended to allow maximal value finding to be computed on the ciphertext data so as to preserve user privacy with good efficiency. Furthermore, such a solution could also be developed for other comparative operations like Average, Percentile and Histogram. Three versions of Maximal value finding (Max) are introduced and analysed in order to differentiate their efficiency and capability to determine the maximum value in a privacy-preserving manner. Moreover, the formal security proof and extensive performance evaluation of our proposed schemes demonstrate that APDA and its extended version can achieve stronger security with an optimised efficiency advantage over the state-of-the-art in terms of both computational and communication overheads. In the MCC environment, the new LHE scheme is proposed with a significant difference so as to allow arbitrary functions to be executed on ciphertext data. Such a scheme will enable rich-mobile applications provided by CSPs to be leveraged by resource-constraint devices in a privacy-preserving manner. The scheme works well as long as noise (a random number attached to the plaintext for security reasons) is less than the encryption key, which makes it flexible. The flexibility of the key size enables the scheme to incorporate with any computation functions in order to produce an accurate result. In addition, this scheme encrypts integers rather than individual bits so as to improve the scheme’s efficiency. With a proposed process that allows three or more parties to communicate securely, this scheme is suited to the MCC environment due to its lightweight property and strong security. Furthermore, the efficacy and efficiency of this scheme are thoroughly evaluated and compared with other schemes. The result shows that this scheme can achieve stronger security under a reasonable cost.

Moving services to the Cloud environment is a trend that has been increasing in recent years, with a constant increase in sophistication and complexity of such services. Today, even critical infrastructure operators are considering moving their services and data to the Cloud. As Cloud computing grows in popularity, new models are deployed to further the associated benefits. Federated Clouds are one such concept, which are an alternative for companies reluctant to move their data out of house to a Cloud Service Providers (CSP) due to security and confidentiality concerns. Lack of collaboration among different components within a Cloud federation, or among CSPs, for detection or prevention of attacks is an issue. For protecting these services and data, as Cloud environments and Cloud federations are large scale, it is essential that any potential solution should scale alongside the environment adapt to the underlying infrastructure without any issues or performance implications. This thesis presents a novel architecture for collaborative intrusion detection specifically for CSPs within a Cloud federation. Our approach offers a proactive model for Cloud intrusion detection based on the distribution of responsibilities, whereby the responsibility for managing the elements of the Cloud is distributed among several monitoring nodes and brokering, utilising our Service-based collaborative intrusion detection – “Security as a Service” methodology. For collaborative intrusion detection, the Dempster-Shafer (D-S) theory of evidence is applied, executing as a fusion node with the role of collecting and fusing the information provided by the monitoring entities, taking the final decision regarding a possible attack. This type of detection and prevention helps increase resilience to attacks in the Cloud. The main novel contribution of this project is that it provides the means by which DDoS attacks are detected within a Cloud federation, so as to enable an early propagated response to block the attack. This inter-domain cooperation will offer holistic security, and add to the defence in depth. However, while the utilisation of D-S seems promising, there is an issue regarding conflicting evidences which is addressed with an extended two stage D-S fusion process. The evidence from the research strongly suggests that fusion algorithms can play a key role in autonomous decision making schemes, however our experimentation highlights areas upon which improvements are needed before fully applying to federated environments.

The heavy demands on cloud computing resources have led to a substantial growth in energy consumption of the data transferred between cloud computing parties (i.e., providers, datacentres, users, and services) and in datacentre’s services due to the increasing loads on these services. From one hand, routing and transferring large amounts of data into a datacentre located far from the user’s geographical location consume more energy than just processing and storing the same data on the cloud datacentre. On the other hand, when a cloud user submits a job (in the form of a set of functional and non-functional requirements) to a cloud service provider (aka, datacentre) via a cloud services broker; the broker becomes responsible to find the best-fit service to the user request based mainly on the user’s requirements and Quality of Service (QoS) (i.e., response time, latency). Hence, it becomes a high necessity to locate the lowest energy consumption route between the user and the designated datacentre; and the minimum possible number of most energy efficient services that satisfy the user request. In fact, finding the most energy-efficient route to the datacentre, and most energy efficient service(s) to the user are the biggest challenges of multi-cloud broker’s environment. This thesis presents and evaluates a novel multi-cloud broker solution that contains three innovative models and their associated algorithms. The first one is aimed at finding the most energy efficient route, among multiple possible routes, between the user and cloud datacentre. The second model is to find and provide the lowest possible number of most energy efficient services in order to minimise data exchange based on a bin-packing approach. The third model creates an energy-aware composition plan by integrating the most energy efficient services, in order to fulfil user requirements. The results demonstrated a favourable performance of these models in terms of selecting the most energy efficient route and reaching the least possible number of services for an optimum and energy efficient composition.