Critical Infrastructures play a central role in the world around us and are the backbone of everyday life. Their service provision has become more widespread, to the point where it is now practically ubiquitous in many societies. Critical Infrastructure assets contribute to the economy and society as a whole. Their impact on the security, economy and health sector are extremely vital. Critical Infrastructures now possess levels of automation that require the integration of, often, mutually incompatible technologies. Their increasing complexity has led to the creation of direct and indirect interdependent connections amongst the infrastructure groupings. In addition, the data generated is vast as the intricate level of interdependency between infrastructures has grown. Since Critical Infrastructures are the backbone of everyday life, their protection from cyber-threats is an increasingly pressing issue for governments and private industries. Any failures, caused by cyber-attacks, have the ability to spread through interconnected systems and are a challenge to detect; especially as the Internet is now heavily reliant on Critical Infrastructures. This has led to different security threats facing interconnected security systems. Understanding the complexity of Critical Infrastructure interdependencies, how to take advantage of it in order to minimize the cascading problem, enables the prediction of potential problems before they happen. Therefore, this work firstly discusses the interdependency challenges facing Critical Infrastructures; and how it can be used to create a support network against cyber-attacks. In much, the same way as the human immune system is able to respond to intrusion. Next, the development of a distributed support system is presented. The system employs behaviour analysis techniques to support interconnected infrastructures and distribute security advice throughout a distributed system of systems. The approach put forward is tested through a statistical analysis methodology, in order to investigate the cascading failure effect whilst taking into account the independent variables. Moreover, our proposed system is able to detect cyber-attacks and share the knowledge with interconnected partners to create an immune system network. The development of the ‘Critical Infrastructure Auto-Immune Response System’ (CIAIRS) is presented with a detailed discussion on the main segments that comprise the framework and illustrates the functioning of the system. A semi-structured interview helped to demonstrate our approach by using a realistic simulation to construct data and evaluate the system output.

The combination of Serious Games and the Internet of Things is a recent academic domain of research. By combining the software and gaming advantages of Serious Games with the interconnected hardware and middleware driven ecosystem of the Internet of Things, it is possible to develop data-driven games that source data from the local or extended physical environment to progress in the virtual environment of gaming. The following thesis presents research into Serious Games and the Internet of Things, focusing on the development of a modular framework that represents the combination of the two technologies. Current research in the domain of Smart Serious Games omits a modular framework that is application independent and outlines the software and hardware interaction between Serious Games and the Internet of Things, therefore this thesis is the first to introduce one. By developing such a framework, this thesis contributes to the academic domain and encourages new and innovative real-world applications of Smart Serious Games that include healthcare, education, simulation and others. Further to the framework, this thesis presents a survey into the network topologies for Serious Games and the Internet of Things and a computer algorithm that provides a measure of student engagement, integrated into a Smart Serious Game developed as part of the undertaken research named Student Engagement Application (SEA). This thesis utilises a semester-long experiment and the techniques of control groups and randomised control trials to investigate the compare the measures of engagement obtained through SEA and self-reflection questionnaires, and the measure of student engagement against academic performance, respectively. After statistical analysis, the data presented strong confidence in the measure of engagement through SEA, validating the effectiveness of the proposed framework for Smart Serious Games.

A crisis is an infrequent and unpredictable event. Training and preparation process requires tools for representation of crisis context. Particularly, crisis events consist of different situations, which can occur at the same time combining into complex situation and becoming a challenge in coordinating several crisis management departments. In this regards, disaster prevention, preparedness and relief can be conceptualized into a design of hypothetical crisis game. Many complex tasks during development of emergency circumstance provide an opportunity for practitioners to train their skills, which are situation analysis, decision-making, and coordination procedures. While the training in physical workouts give crisis personal a hand-on experience in the given situation, it often requires a long time to prepare with a considerable budget. Alternatively, computational framework which allows simulation of crisis models tailoring into crisis scenario can become a cost-effective substitution to this study and training. Although, there are several existing computational toolsets to simulate crisis, there is no system providing a generic functionality to define crisis scenario, simulation model, agent development, and artificial intelligence problem planning in the single unified framework. In addition, a development of genetic framework can become too complex due to a multi-disciplinary knowledge required in each component. Besides, they have not fully incorporated a game technology toolset to fasten the system development process and provide a rich set of features and functionalities to these mentioned components. To develop such crisis simulation system, there are several technologies that must be studied to derive a requirement for software engineering approach in system’s specification designs. With a current modern game technology available in the market, it enables fast prototyping of the framework integrating with cutting-edge graphic render engine, asset management, networking, and scripting library. Therefore, a serious game application for education in crisis management can be fundamentally developed early. Still, many features must be developed exclusively for the novel simulation framework on top of the selected game engine. In this thesis, we classified for essential core components to design a software specification of a serious game framework that eased crisis scenario generation, terrain design, and agent simulation in UML formats. From these diagrams, the framework was prototyped to demonstrate our proposed concepts. From the beginning, the crisis models for different disasters had been analysed for their design and environment representation techniques, thus provided a choice of based simulation technique of a cellular automata in our framework. Importantly, a study for suitability in selection of a game engine product was conducted since the state of the art game engines often ease integration with upcoming technologies. Moreover, the literatures for a procedural generation of crisis scenario context were studied for it provided a structure to the crisis parameters. Next, real-time map visualization in dynamic of resource representation in the area was developed. Then the simulation systems for a large-scale emergency response was discussed for their choice of framework design with their examples of test-case study. An agent-based modelling tool was also not provided from the game engine technology so its design and decision-making procedure had been developed. In addition, a procedural content generation (PCG) was integrated for automated map generation process, and it allowed configuration of scenario control parameters over terrain design during run-time. Likewise, the artificial planning architecture (AI planning) to solve a sequence of suitable action toward a specific goal was considered to be useful to investigate an emergency plan. However, AI planning most often requires an offline computation with a specific planning language. So the comparison study to select a fast and reliable planner was conducted. Then an integration pipeline between the planner and agent was developed over web-service architecture to separate a large computation from the client while provided ease of AI planning configuration using an editor interface from the web application. Finally, the final framework called CGSA-SIM (Crisis Game for Scenario design and Agent modelling simulation) was evaluated for run-time performance and scalability analysis. It shown an acceptable performance framerate for a real-time application in the worst 15 frame-per-seconds (FPS) with maximum visual objects. The normal gameplay performed capped 60 FPS. At same time, the simulation scenario for a wildfire situation had been tested with an agent intervention which generated a simulation data for personal or case evaluation. As a result, we have developed the CGSA-SIM framework to address the implementation challenge of incorporating an emergency simulation system with a modern game technology. The framework aims to be a generic application providing main functionality of crisis simulation game for a visualization, crisis model development and simulation, real-time interaction, and agent-based modelling with AI planning pipeline.

In the blooming era of the Internet of Things (IoT), trust has become a vital factor for provisioning reliable smart services without human intervention by reducing risk in autonomous decision making. However, the merging of physical objects, cyber components and humans in the IoT infrastructure has introduced new concerns for the evaluation of trust. Consequently, a large number of trust-related challenges have been unsolved yet due to the ambiguity of the concept of trust and the variety of divergent trust models and management mechanisms in different IoT scenarios. In this PhD thesis, my ultimate goal is to propose an efficient and practical trust evaluation mechanisms for any two entities in the IoT. To achieve this goal, the first important objective is to augment the generic trust concept and provide a conceptual model of trust in order to come up with a comprehensive understanding of trust, influencing factors and possible Trust Indicators (TI) in the context of IoT. Following the catalyst, as the second objective, a trust model called REK comprised of the triad Reputation, Experience and Knowledge TIs is proposed which covers multi-dimensional aspects of trust by incorporating heterogeneous information from direct observation, personal experiences to global opinions. The mathematical models and evaluation mechanisms for the three TIs in the REK trust model are proposed. Knowledge TI is as “direct trust” rendering a trustor’s understanding of a trustee in respective scenarios that can be obtained based on limited available information about characteristics of the trustee, environment and the trustor’s perspective using a variety of techniques. Experience and Reputation TIs are originated from social features and extracted based on previous interactions among entities in IoT. The mathematical models and calculation mechanisms for the Experience and Reputation TIs also proposed leveraging sociological behaviours of humans in the real-world; and being inspired by the Google PageRank in the web-ranking area, respectively. The REK Trust Model is also applied in variety of IoT scenarios such as Mobile Crowd-Sensing (MCS), Car Sharing service, Data Sharing and Exchange platform in Smart Cities and in Vehicular Networks; and for empowering Blockchain-based systems. The feasibility and effectiveness of the REK model and associated evaluation mechanisms are proved not only by the theoretical analysis but also by real-world applications deployed in our ongoing TII and Wise-IoT projects.

Along with the many benefits of IoT, its heterogeneity brings a new challenge to establish a trustworthy environment among the objects due to the absence of proper enforcement mechanisms. Further, it can be observed that often these encounters are addressed only concerning the security and privacy matters involved. However, such common network security measures are not adequate to preserve the integrity of information and services exchanged over the internet. Hence, they remain vulnerable to threats ranging from the risks of data management at the cyber-physical layers, to the potential discrimination at the social layer. Therefore, trust in IoT can be considered as a key property to enforce trust among objects to guarantee trustworthy services. Typically, trust revolves around assurance and confidence that people, data, entities, information, or processes will function or behave in expected ways. However, trust enforcement in an artificial society like IoT is far more difficult, as the things do not have an inherited judgmental ability to assess risks and other influencing factors to evaluate trust as humans do. Hence, it is important to quantify the perception of trust such that it can be understood by the artificial agents. In computer science, trust is considered as a computational value depicted by a relationship between trustor and trustee, described in a specific context, measured by trust metrics, and evaluated by a mechanism. Several mechanisms about trust evaluation can be found in the literature. Among them, most of the work has deviated towards security and privacy issues instead of considering the universal meaning of trust and its dynamic nature. Furthermore, they lack a proper trust evaluation model and management platform that addresses all aspects of trust establishment. Hence, it is almost impossible to bring all these solutions to one place and develop a common platform that resolves end-to-end trust issues in a digital environment. Therefore, this thesis takes an attempt to fill these spaces through the following research work. First, this work proposes concrete definitions to formally identify trust as a computational concept and its characteristics. Next, a well-defined trust evaluation model is proposed to identify, evaluate and create trust relationships among objects for calculating trust. Then a trust management platform is presented identifying the major tasks of trust enforcement process including trust data collection, trust data management, trust information analysis, dissemination of trust information and trust information lifecycle management. Next, the thesis proposes several approaches to assess trust attributes and thereby the trust metrics of the above model for trust evaluation. Further, to minimize dependencies with human interactions in evaluating trust, an adaptive trust evaluation model is presented based on the machine learning techniques. From a standardization point of view, the scope of the current standards on network security and cybersecurity needs to be expanded to take trust issues into consideration. Hence, this thesis has provided several inputs towards standardization on trust, including a computational definition of trust, a trust evaluation model targeting both object and data trust, and platform to manage the trust evaluation process.

Sickle cell disease (SCD) is a common serious genetic disease, which has a severe impact due to red blood cell (RBCs) abnormality. According to the World Health Organisation, 7 million newborn babies each year suffer either from the congenital anomaly or from an inherited disease, primarily from thalassemia and sickle cell disease. In the case of SCD, recent research has shown the beneficial effects of a drug called hydroxyurea/hydroxycarbamide in modifying the disease phenotype. The clinical management of this disease-modifying therapy is difficult and time consuming for clinical staff. This includes finding an optimal classifier that can help to solve the issues with missing values, multi-class datasets, and features selection. For the classification and discriminant analysis of SCD datasets, 7 classifiers based on machine learning models are selected representing linear and non-linear methods. After running these classifiers with a single model, the results revealed that a single classifier has provided us with effective outcomes in terms of the classification performance evaluation metric. In order to produce such an optimal outcome, this research proposed and designed combined classifiers (ensemble classifiers) among the neural network’s models, the random forest classifier, and the K-nearest neighbour classifier. In this aspect, combining the levenberg-marquardt algorithm, the voted perceptron classifier, the radial basis neural classifier, and random forest classifier obtain the highest rate of performance and accuracy. This ensemble classifier receives better results during the training set and testing set process. Recent technology advances based on smart devices have improved the medical facilities and become increasingly popular in association with real-time health monitoring and remote/personal health-care. The web-based system developed under the supervision of the haematology specialist at the Alder Hey Children’s Hospital in order to produce such an effective and useful system for both patients and clinicians. To sum up, the simulation experiment concludes that using machine learning and the web-based system platforms represents an alternative procedure that could assist healthcare professionals, particularly for the specialist nurse and junior doctor to improve the quality of care with sickle cell disorder.

Casual-Leisure Search describes any behaviour that allows people to express and satisfy hedonistic needs rather than information needs as part of the information-seeking process. For example, individuals who search their social media universe for hours after a long day at work may do so out of curiosity, to relax or for fun (e.g. exploring for the experience). Studies have shown that classical information seeking (IS) and interactive information retrieval models (IIR) have failed to represent them because they were created observing people in work related scenarios, and assuming that search is always a rational decision making process and with an extrinsic utilitarian value. The research described in this PhD work investigates IIR from the perspective of the psychological curiosity and leisure information seeking behaviour. Traditional search engines focus the user experience on satisfying users with topically relevant information (i.e. quick lookup search and then moving on), but they are limited supporting the discovery of unknown information because they fail to entice and engage users exploration as proxy to seek enjoyment both in leisure and work scenarios. The research described increases understanding of the role that curiosity plays in IIR and investigates the merits of incorporating the characteristics and function of human curiosity in the design of IIR systems. The research is grounded by the theoretical understanding of how human curiosity works. A review of appropriate psychological curiosity literature offers a means to critique existing IIR tools and a basis from which to start designing novel curiosity driven search tools. In the first experimental work, this research compared IIR behaviour between a standard query response paradigm and a curiosity driven search map prototype using social media content, and attempts to learn lessons from the behaviour that people show in everyday casual-leisure search scenarios. In the second experiment, this research contrast IIR behaviour between standard query-response paradigm and a curious adaptation of query-response paradigm using search notifications or recommendations for news reading in a social media leisure search scenario. The tools are evaluated to determine the usefulness of incorporating curiosity in the design of IIR systems, to learn about the effect in user engagement, how users exploration is increase when motivated by a hedonistic need, and then elaborate a set of design recommendations to enhance the search experience in leisure scenarios.

Hand pose is emerging as an important interface for human-computer interaction. The problem of hand pose estimation from passive stereo inputs has received less attention in the literature compared to active depth sensors. This thesis seeks to address this gap by presenting a data-driven method to estimate a hand pose from a stereoscopic camera input, with experimental results comparable to more expensive active depth sensors. The frameworks presented in this thesis are based on a two camera stereo rig capture as it yields a simpler and cheaper set-up and calibration. Three frameworks are presented, describing the sequential steps taken to solve the problem of depth and pose estimation of hands. The first is a data-driven method to estimate a high quality depth map of a hand from a stereoscopic camera input by introducing a novel regression framework. The method first computes disparity using a robust stereo matching technique. Then, it applies a machine learning technique based on Random Forest to learn the mapping between the estimated disparity and depth given ground truth data. We introduce Eigen Leaf Node Features (ELNFs) that perform feature selection at the leaf nodes in each tree to identify features that are most discriminative for depth regression. The system provides a robust method for generating a depth image with an inexpensive stereo camera. The second framework improves on the task of hand depth estimation from stereo capture by introducing a novel superpixel-based regression framework that takes advantage of the smoothness of the depth surface of the hand. To this end, it introduces Conditional Regressive Random Forest (CRRF), a method that combines a Conditional Random Field (CRF) and a Regressive Random Forest (RRF) to model the mapping from a stereo RGB image pair to a depth image. The RRF provides a unary term that adaptively selects different stereo-matching measures as it implicitly determines matching pixels in a coarse-to-fine manner. While the RRF makes depth prediction for each super-pixel independently, the CRF unifies the prediction of depth by modeling pair-wise interactions between adjacent superpixels. The final framework introduces a stochastic approach to propose potential depth solutions to the observed stereo capture and evaluate these proposals using two convolutional neural networks (CNNs). The first CNN, configured in a Siamese network architecture, evaluates how consistent the proposed depth solution is to the observed stereo capture. The second CNN estimates a hand pose given the proposed depth. Unlike sequential approaches that reconstruct pose from a known depth, this method jointly optimizes the hand pose and depth estimation through Markov-chain Monte Carlo (MCMC) sampling. This way, pose estimation can correct for errors in depth estimation, and vice versa. Experimental results using an inexpensive stereo camera show that the proposed system measures pose more accurately than competing methods. More importantly, it presents the possibility of pose recovery from stereo capture that is on par with depth based pose recovery.

Where improving the safety or performance of a system, there is a tendency to focus on negative aspects surrounding human performance or interaction: errors, threats, past incidents or identified issues and flaws. This does not, however, tell the whole story. Users frequently deploy a variety of resilient interventions, devising and implementing strategies to improve performance and mitigate threats such as errorparticularly during complex or challenging circumstances. In so doing, users can and do make an active, positive contribution to the wider resilience of a system. To date, the subject of how individual actors within a system leverage such resilience strategies to improve the functioning of said system is a topic that has received only limited direct investigation. An initial study was undertaken as a probing investigation to test the notion of user-configured cues as a means to facilitate individual resilience. The insights from this study challenged an existing foundational categorisation scheme, which we then sought to expand and refine in collaboration with its original authors, to better represent and articulate 10 different types of resilience strategy. As a means to broaden our real-world pool of strategy accounts, a diary study was then conducted, the resulting data being used to both inform and validate a new iteration of the scheme. Stemming from challenges of the applicability of the scheme to complex resilience cases, we introduced the notion of a new type of compound strategy, and developed a framework to support their analysis by deconstructing them to examine their motivational and functional components. A final controlled laboratory study was undertaken to apply our insights. The resultant refined categorisation scheme and conceptual framework enrich our understanding of the phenomenon of user or individual resilience and could potentially be leveraged to inform and support the design of future technical and sociotechnical systems.

Humans combine their senses to enhance the world around them. While computers have evolved to reflect these sensory demands, only the primary senses of vision and audition (and to an extent, touch) are used in modern communication. This thesis investigated how additional information, such as emotion and navigational assistance, might be communicated using technology-based implementations of sensory displays that output the non-primary modalities of smell, vibrotactile touch, and thermo-touch. This thesis explored using a portable atomiser sprayer to deliver emotional information via smell to mobile phone users, a ring-shaped device worn on the finger to display emotional information using vibration and colours, and an array of thermoelectric coolers worn on the arm to create temperature sensations. Additionally, this thesis explored two methods of signalling temperature using the thermal implementation, and finally, used it in a controlled study to augment the perceived emotion of text messages using temperature. There were challenges with using some of these implementations to display information. Smells produced with the scent technology were ambiguous and highly cognitive, and poor delivery to the user produced undesirable cross-adaption effects when smells lingered and mixed in the environment. The device used to communicate vibrotactile and colour lighting cues neutralized emotions in text messages. Furthermore, temperature pattern discrimination using the thermal implementation was difficult due to non-linear interaction effects that occurred on the skin’s surface, as well as latency resulting from the thermal neurological pathway and the technology used to heat and cool the skin. However, the thermal implementation enabled more accurate user discrimination between thermal signals than what a single stimulator design provided. Furthermore, the utility of continuous thermal feedback, in the context of spatial navigation, was demonstrated, which improved user performance compared to when the user was not presented with any thermal information. Finally, temperature was demonstrated to elicit arousal reactions across subjects using the thermal implementation, and could augment the arousal of text messages, especially when the content of the message was strongly neutral. However, no similar statistical significance was observed with valence, demonstrating the complex implications of using thermal cues to convey emotional information.