Design is all about visuals, or that is what I have found out during this thesis, from the process materials to the outcome our main entry point to any problem is how will we solve it visually so it’s understandable for the general user. This aspect is problematic in itself due to the fact that we, as humans, understand the world and the things around using all our senses continuously, even though we can forget as visuals are so overpowering. There is a huge opportunity area in exploring our other senses and bringing them back to technology, and this can be seen in works in the past like Tangible Interactions [1] or Natural User Interfaces [2]. But in this moment in time, where all these new technologies like VR/AR and IoT are about to enter our lives and change them forever, this topic is more important than ever. We have already seen what happens when we turn humans into mere machines with some fingers as interactive inputs, and barely any senses to process all the information given to us. Now that these technologies are still young and malleable, we can direct the future to where we want it instead of being guided by the technology itself. To do this we need to reimagine the design process, not reinvent the wheel, but add experts which we currently leave behind and I argue are key to unlock these technologies, experts not only of the technological side of things but on the human side too, like physiotherapists and dancers. Add also people who we never think about when we think of VR like visually impaired users, which could make these technologies inclusive since early on, instead of as an afterthought like we usually do. Not only people, but we also need to add new materials to understand how we use our senses and explore ways that we can understand and explore them differently; like bodystorming and improv theatre because when things aren’t visual, how do you sketch it? A sketch turns into a video about movement. The end result provides a wide breadth of examples of the types of innovations that can come out of using these new design materials, and to open new frontiers. From a VR game with no visuals whatsoever to an AR location based story game, to a home sized multimodal operating system containing several different apps controlled through physical movement. The examples open up the space instead of closing into a single solution. This is just the tip of the iceberg, a hope that others will be inspired by it and continue with this journey that has just started, to guide the future into one that is more technological and at the same time more human than ever before. What we know is that VR does not equate Visual Reality.

In this thesis viewpoints of Hegel's philosophy of history were confronted with selected fields of 20th century social theory. The Enlightenment was chosen as a keyword. In Hegel's Phenomenology of Mind the Enlightenment is a mode of experience of consciousness, or a phenomenon of Spirit in its history, a phenomenon which is becoming outdated in his time. To illustrate such overcome this conception of Enlightenment is demonstrated on Kant's article The answer to a Question: What is Enlightenment?. Thesis then examines the way in which had Hegel's interpretation inspired Critical theory - specifically Adorno's and Horkheimer's Dialectic of Enlightenment and Marcuse's Reason and Revolution. The last part of thesis is focused on the broader field of social theory to find manifestations of thought stream begun by Hegel. It finds it out especially in thought of Gadamer in considerably radical form calling to a question the epistemology of humanities till his present. Viewpoints emerged from Hegel's philosophical reaction to the Enlightenment represent still current counterpart of neopositivist approaches and a relevant reference point in epistemological uncertainty and in finding out an adequate socio-historical role of humanities.

Telehealth is an online health care system that is extensively used in the current pandemic situation. Our proposed technique is considered a fog computing-based attack detection architecture to protect IoT Telehealth Networks. As for IoT Telehealth Networks, the sensor/actuator edge devices are considered the weakest link in the IoT system and are obvious targets of attacks such as botnet attacks. In this thesis, we introduce a novel framework that employs several machine learning and data analysis techniques to detect those attacks. We evaluate the effectiveness of the proposed framework using two publicly available datasets from real-world scenarios. These datasets contain a variety of attacks with different characteristics. The robustness of the proposed framework and its ability, to detect and distinguish between the existing IoT attacks that are tested by combining the two datasets for cross-evaluation. This combination is based on a novel technique for generating supplementary data instances, which employs GAN (generative adversarial networks) for data augmentation and to ensure that the number of samples and features are balanced. / Graduate

<p>The accelerated development of mobile computational platforms and artificial intelligence (AI) has led to increase in interconnected products with sensors that are creating smart environments. The smart environment expands the interactive spaces from limited digital screens, such as desktops and phones, to a much broader category that includes everyday objects, smart things, surrounding contexts, robots, and humans. The improvement of personal computing devices including smartphones, watches, and AR glasses further broadens the communication bandwidth between us and the ambient intelligence from the surrounding environment. Additionally in this smart environment people want to pursue personalization and are motivated to design and build their own smart environments and author customized functions.</p> <p> </p> <p>My work in this thesis focuses on investigating workflows and methods to support end-users to create personalized interactive experiences within smart environments. In particular, I designed the authoring systems by inspecting different interaction modalities, namely the direct input, spatial movement, in-situ activity and embodied interactions between users and everyday objects, smart things, robots and virtual mid-air contents. To this end, we explored 1) the software tools, hardware modules, and machines that support users to augment non-smart environments with digital interfaces and functions, and 2) the intelligence and context-awareness powered by the smart environments that deliver automatic and assistance during living and entertaining experiences. In this thesis, I mainly studied the following authoring workflows and systems: 1) customizing interactive interfaces on ordinary objects by surface painted circuits, 2) constructing a spatially aware environment for service robots with IoT modules, 3) authoring robot and IoT applications that can be driven by body actions and daily activities and 4) creating interactive and responsive augmented reality applications and games that can be played through natural input modalities.</p> <p> </p> <p>Takeaways from the main body of the research indicate that the authoring systems greatly lower the barrier for end-users to understand, control, and modify the smart environments. We conclude that seamless, fluent, and intuitive authoring experiences are crucial for building harmonious human-AI symbiotic living environments.</p>

Due to the influence of information fusion in Industrial Internet of Things (IIoT) environments, there are many problems, such as weak intelligent visual target positioning, disappearing features, large error in visual positioning processes, and so on. Therefore, this paper proposes a weak target positioning method based on multi-information fusion, namely the “confidence interval method”. The basic idea is to treat the brightness and gray value of the target feature image area as a population with a certain average and standard deviation in IIoT environments. Based on the average and the standard deviation, and using a reasonable confidence level, a critical threshold is obtained. Compared with the threshold obtained by the maximum variance method, the obtained threshold is more suitable for the segmentation of key image features in an environment in which interference is present. After interpolation and de-noising, it is applied to mobile weak target location of complex IIoT systems. Using the metallurgical industry for experimental analysis, results show that the proposed method has better performance and stronger feature resolution.

Intrusion detection systems (IDSs) play a crucial role in the identification and mitigation for attacks on host systems. Of these systems, vehicular ad hoc networks (VANETs) are particularly difficult to protect due to the dynamic nature of their clients and their necessity for constant interaction with their respective cyber-physical systems. Currently, there is a need for a VANET-specific IDS that can satisfy these requirements. Spline function-based IDSs have shown to be effective in traditional network settings. By examining the various construction of splines and testing their robustness, the viability for a spline-based IDS can be determined.

Cloud computing is about delivery of different computing services involving databases, analytics, software, networking with the use of internet to enhance innovation, incorporate flexibility in resources and broaden profitability. However, Internet of Things (IoT) is an essential system for interrelating computer devices, digital machines, people and others which are offered with unique identifiers where data can be transferred with human involvement and wireless network. 42% of organisations in UK use cloud computing. The problem with cloud computing revolves around security and privacy issues as data is stored by a third party from inside or outside of the organisation leading to broken authentication, compromising of credentials and others. The use of IoT is vulnerable as it provides connectivity to devices, machines and people therefore, it needs to contain more storage that is made from cloud facilities. Survey has been conducted where primary quantitative method has been considered to obtain data from 101 managers of the organisation that has adopted cloud computing and IoT. However, 8 close-ended questions have been asked to 101 managers. Positivism philosophy has been used to make quantifiable observations along with descriptive design and others. The results and discussion will analyse responses of the respondents after conducting statistical analysis. However, research has been revolving around making a comparison between using cloud computing and IoT along with analysing organisational behaviour. / Revisión por pares

The application of Internet of Things (IoT) where sensors and actuators embedded in physical objects are linked through wired and wireless networks has shown a rapid growth over the past years in various domains with the benefits of improving efficiency and productivity, reducing cost, providing mobility and agility, etc. This dissertation focuses on developing signal processing and machine learning based techniques in IoT with applications to 1) smart energy generation and 2) robust indoor localization in smart city. Smart grids, in contrast to legacy grids, facilitate more efficient electricity generation and consumption by allowing two-way information exchange among various components in the grid and the users based on the measurements from numerous sensors located at different places. Due to the introduction of information communications, a smart grid is faced with the risk of external attacks which is aimed to take control of the grid. In particular, electricity generation from photovoltaic (PV) systems is a mature power generation technology utilizing renewable resources, owning to its advantages in clean production, reduced cost and high flexibility. However, the performance of a PV system can be susceptible and unstable due to various physical failures and dynamic environments (internal circuit faults, partial shading, etc.). To safeguard the system security, fault or attack detection technologies are of great importance for PV systems and smart grids. Existing approaches on fault or attack detection either rely on the prediction by a predetermined system model which acts as reference data for comparison or can be applied only within a certain set of component (e.g., several PV strings) based on local statistical properties without the capability of generalization. Furthermore, the output performance of a PV system is dynamic under different environmental conditions (irradiance level, temperature, etc.), which can be optimized by the technique of maximum power point tracking (MPPT). However, previous studies on MPPT usually require prior knowledge of the system model or high computational complexity for iterative optimization. Smart city, as another important application of IoT, relies on analysis of the measurement data from sensors located at users and environments to provider intelligent solutions in our daily life. One of the fundamental tasks for advanced location-based services is to accurately localize the user in a certain environment, e.g., on a certain floor inside a building. Indoor localization is faced with challenges of moving users, limited availability of sensors and noisy measurements due to hardware constraints and external interferences. This dissertation first describes our advanced fault/attack detection and localization methods for PV systems and smart grids, then develops our enhanced MPPT techniques for PV systems, and finally presents our robust indoor localization methods for smartphone users, based on statistical signal processing and machine learning approaches. In Chapter 2 and Chapter 3, we proposes fault/attack detection method in PV systems and smart grids respectively in the framework of abrupt change detection utilizing sequential output measurements without assuming any prior knowledge of the system characteristics or particular faulty/attack patterns, such that an alarm will triggered regardless of the magnitude or the type of faulty/attack signals. Starting from the proposed fault detection method in Chapter 2, we present our fault localization method for PV systems in Chapter 4 where the central controller is able to identify the faulty PV strings without full knowledge of each local measurements. Chapter 5 studies the MPPT method under dynamic shading conditions where we adopt neural networks to assist the identification of the global maximum power point by depicting the relationship between the system output power and the operating voltage. In Chapter 6, to tackle the challenge of accurate and robust indoor localization for smart city when sensors provides noisy measurement data, we propose a cooperative localization method which exploits the readings of the received strengths of Wi-Fi signals at the smartphone users and the relative distances among neighboring users to combat the deterioration due to aggregated measurement errors. Throughout the dissertation, our proposed methods are followed by simulations (of a PV system or a grid under various operating conditions) or experiments (of localizing moving users with smartphones to record sensors' measurements). The results demonstrate that our proposed fault/attack detection and localization methods and MPPT schemes can achieve higher adaptivity and efficiency with robustness against various external conditions an lower computational complexity, and our cooperative localization methods have high localization accuracy even given large measurement errors and limited measurement data.

The current network trends point towards a significant discrepancy between the data usage and the underlying architecture; a severely increasing amount of data is being sent from more devices while data usage is becoming more data-centric instead of the previously host-centric. Information Centric Network (ICN) is a new alternative network paradigm that is designed for a data-centric usage. ICN is based on uniquely naming data packages and making it location independent. This thesis researched how to implement an efficient naming for ICN in a Smart Home Scenario. The results are based on testing how the forwarding information base is populated for numerous different scenarios and how a node's duty cycle affects its power usage. The results indicate that a hierarchical naming is optimized for hierarchical-like network topology and a flat naming for interconnected network topologies. An optimized duty cycle is strongly dependent on the specific network and accordingto the results can a sub-optimal duty cycle lead to excessive powerusage.

As the rise of Industry 4.0 sheds light on many emerging technologies, oursociety will change with it. While it brings forth many positive aspects, itcannot be ignored the socio-economic problems we may face in the future.Many jobs will be transformed, manual labour such as order picking, forkliftdriving will be vanishing, and humanity will have to adapt, as we have for theprevious industrial revolutions.Educating the industrial workers that face unemployment due to automationis an important ethical matter, but can we as humans develop our knowledgewith the technology, as opposed to adapting to it? This thesis uses methodsof interaction design to create an alternative educational format, forindustrial workers to learn about the Internet of Things, an essentialcomponent of Industry 4.0.The result of this is TIOTTA (Teaching Internet of Things ThroughApplication), a contextual learning material designed together with industrialworkers.