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"Smart grid" generally refers to a class of technology bringing electricity delivery systems into the 21st century, using computer-based remote control and automation. With the growing energy demand, efficient usage of the available energy resources is increasingly becoming a major issue around the world. Smart grid is a step in that direction. Research in the European Union and the United States are currently underway to modernize the existing and aging transmission grid and to streamline the usage of electricity. A typical electricity grid consists of two major entities - the utility company and the distribution control system (DCS). Electricity is generated at the utility company and the DCS is responsible for the distribution of electricity to individual homes/consumers. A smart meter (SM) is an electronic device that measures the electricity consumed at the consumer's premises and provides added information to the utility company. The data concentration unit (DCU) is a device acting as a communication hub collecting and encoding data from multiple smart meters in a neighborhood and forwarding the data to the utility company. The aim of this project is to design a network for securing the communication between the SM and the DCU in a smart metering network environment. The meter data communicated from the SM to the DCU is very sensitive and in the hands of an attacker, can reveal significant personal information about an individual. Hence it is of at most importance to protect the meter data transmitted from the SM. On the other hand the control signals transmitted from the DCU to the SM, need protection in order to thwart off unauthorized signals (i.e., an intruder can impersonate the DC and send out control signals to the SMs). Hence the SM and the DCU should be authenticated by each other and authorized and the data and/or control signals exchanged between them should be encrypted. / "Smart grid" avser i allmänhet en klass av teknik föra system elleverans till 21: a århundradet, med hjälp av datorbaserade fjärrkontroll och automation. Med den ökande efterfrågan på energi, är effektiv användning av de tillgängliga energiresurser blir alltmer en viktig fråga över hela världen. Smart grid är ett steg i den riktningen. Forskning i Europeiska unionen och USA för närvarande pågår för att modernisera befintliga och åldrande transmissionsnätet och effektivisera användningen av el. En typisk elnätet består av två större enheter - de allmännyttiga företaget och \distribution control system"(DCS). El genereras vid verktyget företaget och DCS ansvarar för distributionen av el till enskilda hem / konsumenter. En smart meter (SM) är en elektronisk apparat som mäter elförbrukning på konsumentens lokaler och ger ökad information till elbolaget. \Data concentration unit"(DCU) är en enhet fungerar som ett kommunikationsnav insamling och kodning av data från flera smarta mätare i ett område och vidarebefordra data till elbolaget. Syftet med detta projekt är att utforma ett nätverk för att säkra kommunikationen mellan SM och DCU i ett smart mätning nätverksmiljö. Mätaren uppgifter som lämnas från SM till DCU är mycket känslig och i händerna på en angripare, kan avslöja viktig personlig information om en individ. Följaktligen är det av som mest betydelse för att skydda de mätdata som sänds från SM: en. å andra sidan styrsignaler överförs från DCU till SM och behöver skydd för att hindra av obehöriga signaler (dvs en inkräktare kan personifiera DC och skicka ut styrsignaler till SM). Därför SM och DCU ska bestyrkas av varandra och godkänts och data och / eller styrsignaler utväxlas mellan dem ska vara krypterad.

<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>

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.

Electric Vehicles (EVs) reduce dependency on oil and carbon emissions. An upsurge in demand for EVs could lead to negative impacts on the grid. However, charging strategies, such as supporting the grid using vehicle-to-grid (V2G) and smart charging technology, can go a long way to reducing the impacts on the electrical load curve. The thesis presents a number of aspects which relate to the interconnection between EVs and the electric grid for achieving an optimized integration. An adaptive algorithm has been developed to perform load peak shaving by V2G and smart charging, while a hypothetical case study containing several types of EVs in a local grid has been conducted. The aim is to examine the developed algorithm. In conclusion, by using the adaptive algorithm, written in C++, an optimum status has been achieved concerning the electric grid and EV batteries.

This research investigates how the new technology implementation in the transportation system in Hangzhou, China affected the work routines and the challenges reported by the managers of the system as well as their suggestions on improvements. Through the analysis of the interviews with the managers in the intelligent transportation system, the main effects of RFID on the development of intelligent transportation in Hangzhou were proposed. It also reveals the current status of RFID development in the intelligent transportation system, further proposes factors that affect the development of RFID, and reveals the core factors that affect the development of Hangzhou's intelligent transportation. This research can enrich the information management theory of the development of an intelligent transportation system, and have certain guidance and reference significance for the development of urban information system at the same time. In this study, through direct communication with the managers of the main intelligent transportation departments in Hangzhou, this research analyzes the influencing factors of the development of RFID in Hangzhou. Based on the analysis results, this research put forward the strategy of urban informatization development in Hangzhou. The researchhas certain practical significance for improving the overall development level of smart transportation in Hangzhou. The impact of RFID on the daily work of managers of intelligent transportation management departments has first improved the innovation of intelligent transportation. Second, improve management efficiency. Third, provide the management with more intelligence plans to solve the impact of eight aspects including transportation problems However, it can be seen from the results of encoding and passing that the senior managers' lack of awareness, including learning information technology, information management capabilities, and other factors, has led to insufficient application of RFID technology in the field of smart transportation in Hangzhou. In response to this phenomenon, three main suggestions were put forward, attach importance to infrastructure construction, improve the information literacy of grassroots employees, and increase the intensity of traffic information management in Hangzhou.

I den fluktuerande digitaliseringsvågen har den fjärde industriella revolutionen eller Industry4.0 initierat inom tillverkningsindustrin vilket påskyndar företag att anpassa och förändra helaverksamheter för att vara fortsatt konkurrenskraftiga. Industrial Internet of Things (IIoT) harblivit en central del av denna förändring för tillverkningsföretag och kan förklaras som företagsom utnyttjar enheter för att samla data i realtid och i sin tur gå mer mot den smarta fabriken.En rad olika möjligheter kan genomföras för industrier med uppgången av IIoT, även omframgången med denna förändring kan variera mellan olika företag beroende på storlek,resurser och ekonomisk stabilitet. Parallellt med möjligheterna uppstår även utmaningar förföretag, särskilt små och medelstora företag, då dessa saknar ekonomiska resurser och storlekför att kunna omfördela och omvandla sin verksamhet. I denna studie har målet varit att skildrahur medelstora tillverkningsföretag hanterar implementeringen av IIoT och den smartafabriken för att anpassa sig till det ständigt föränderliga tekniska paradigm som Industry 4.0har introducerat. Slutsatser har dragits utifrån kombinationen av en teoretisk ram ochintervjuer med sex svenska medelstora tillverkningsföretag. Digitaliseringsstrategier förtillverkningsföretag varierar beroende på bransch. Det finns emellertid enighet om att insatserför en digitaliserad produktion måste ske för att förbli konkurrenskraftig där automatisering,övervakning och kontroll av processer inom IIoT är nyckelfaktorer för att förblikonkurrenskraftiga. Tidsplanen och implementeringsnivån kan också variera beroende pådigital kompetens och motståndskraft mot förändring från personalen. Viktigt att poängtera äratt sambandet mellan IIoT, digitalisering och ökad konkurrenskraft inte är de enda faktorernasom krävs utan det finns fler faktorer att beakta. Studien pekar även på att konkurrensfördelarsällan är det främsta skälet till att företag väljer att digitalisera och implementera IIoT. / In the fluctuating wave of digitization, the fourth industrial revolution or Industry 4.0 in themanufacturing industry, has begun that has accelerated industries and companies to adapt andchange their whole business to maintain competitive. Industrial Internet of Things (IIoT) hasbecome a central part of this change for manufacturing companies and can be interpreted ascompanies taking advantage of units to gather real-time data and in turn, lean towards thesmart factory. A range of possibilities can be accessed by industries with the rise of IIoT,though the success of this change can differ between different companies depending on size,resources, and economic stability. Parallel to the opportunities, challenges arises forcompanies, especially small and middle-sized enterprises, that lack the economic resourcesand scale to redistribute and transform their business. In this paper, the goal has been todistinguish how middle-sized manufacturing companies handle the implementation of IIoT andthe smart factory in order to adapt to the ever-changing technical paradigm that Industry 4.0has introduced. Conclusions have been drawn from the combination of a theoretical frameworkand interviews with six Swedish middle-sized manufacturing companies. The digitizationstrategy for manufacturing companies varies from industries. However, there is a consensusthat efforts towards a digitized production must take place in order to stay competitive whereautomation-, monitoring-, and controlling processes within IIoT are main factors to staycompetitive. The pace and level of implementation can also differ depending on digitalqualification and resistance to change from the staff. Important to note is that the relationbetween IIoT, digitization and increased competitiveness is not the only factors that aresignificant as there are more things to consider. The study also shows that competitiveadvantages are rarely the main reason why companies choose to digitize and implement IIoT.

Over 90% of faculty members in higher education have access to smart mobile devices. However, data are lacking about community college faculty members’ use of smart mobile devices and applications for instruction and content delivery. The purpose of this study was to examine Tennessee community college full-time faculty’s use of smart mobile devices, to determine if there were any significant differences in the mean scores measuring attitudes and use of smart mobile devices by generational age grouping, teaching discipline, rank, years of teaching and to determine if Tennessee community college faculty members who under-utilize mobile technologies for teaching also hold negative opinions about them. This study measured Tennessee Community College faculty use of smart mobile devices and their attitudes and use of smart mobile devices by generational age groups, teaching discipline, rank, and years of teaching. This study used quantitative, nonexperimental survey design. The survey instrument was an electronic questionnaire, consisting of 15 items that were divided into 7 dimensions. The dimensions were: Learning Preference, Institutional Training, Frequency, Attitude, Willingness to Attend PD Training, Willingness to Use, and Competence. Of the 267 possible participants, 93 (35%) responded to the survey. Data from the survey were used to analyze 5 research questions and 35 null hypotheses. Two research questions were analyzed using independent-samples t test 2 and 3 analyzed using one-way analysis of variance. Testing the null hypotheses associated with the 5 research questions resulted in 7 significant findings and 28 findings that were not significant. The findings indicated that there were significant differences in professional development training scores by generational age, and by academic rank. There were significant findings in learning preference by teaching discipline and training by teaching disciplines. Last, there were significant differences in some of the dimensions by years of experience. The results of this study may benefit administrators and educators in knowing what groups are open to professional development training for using smart mobile devices for instruction and in what areas to provide training.

L’autonomicité - la capacité des systèmes à se gérer eux-mêmes - est une qualité nécessaire pour parvenir à contrôler des systèmes complexes, c’est à dire des systèmes ouverts, à grande échelle, dynamiques, composés de sous-systèmes tiers hétérogènes et suivant de multiples objectifs, éventuellement en conflit. Dans cette thèse, nous cherchons à fournir des supports génériques et réutilisables pour la conception de tels systèmes autonomiques complexes. Nous proposons une formalisation des objectifs de gestion, une architecture générique pour la conception de systèmes autonomiques multi-objectifs et adaptables, et des organisations génériques pour l’intégration de tels systèmes autonomiques. Nous appliquons nôtre approche au cas d’utilisation des réseaux électriques intelligents, qui sont un parfait exemple de complexité. Nous présentons une plateforme de simulation que nous avons développée et via laquelle nous illustrons nôtre approche, au travers de plusieurs scénarios de simulation. / Autonomic features, i.e. the capability of systems to manage themselves, are necessary to control complex systems, i.e. systems that are open, large scale, dynamic, comprise heterogeneous third-party sub-systems and follow multiple, sometimes conflicting objectives. In this thesis, we aim to provide generic reusable supports for designing complex autonomic systems. We propose a formalisation of management objectives, a generic architecture for designingadaptable multi-objective autonomic systems, and generic organisations integrating such autonomic systems.We apply our approach to the concrete case of smart micro-grids which is a relevant example of such complexity. We present a simulation platform we developped and illustrate our approach via several simulation scenarios.

Les Smart Grids sont des réseaux électriques intelligents permettant d’optimiser la production, la distribution et la consommation d’électricité grâce à l’introduction des technologies de l’information et de la communication sur le réseau électrique. Les Smart Grids impactent fortement l’ensemble de l’architecture d’entreprise des gestionnaires de réseaux électriques. Simuler une architecture d’entreprise permet aux acteurs concernés d’anticiper de tels impacts.Dès lors, l’objectif de cette thèse est de fournir des modèles, méthodes et outils permettant de modéliser puis de simuler une architecture d’entreprise afin de la critiquer ou de la valider.Dans ce contexte, nous proposons un framework multi-vues, nommé ExecuteEA, pour faciliter la modélisation des architectures d’entreprise en automatisant l’analyse de leurs structures et de leurs comportements par la simulation. ExecuteEA traite chacune des vues métier, fonctionnelle et applicative selon trois aspects : informations, processus et objectifs. Pour répondre au besoin d’alignement métier/IT, nous introduisons une vue supplémentaire : la vue intégration. Dans cette vue nous proposons de modéliser les liens de cohérence inter et intra vues.Nous mettons, par ailleurs, à profit des techniques issues de l’ingénierie dirigée par les modèles en tant que techniques support pour la modélisation et la simulation d’une architecture d’entreprise. Notre validons ensuite notre proposition à travers un cas métier Smart Grid relatif à la gestion d’une flotte de véhicules électriques. / In this thesis, we propose a framework that facilitates modeling Enterprise Architectures (EA) by automating analysis, prediction, and simulation, in order to address the key issue of business/IT alignment. We present our approach in the context of Smart Grids, which are power grids enabled with Information and Communication Technologies. Extensive studies try to foresee the impact of Smart Grids on electric components, telecommunication infrastructure, and industrial automation and IT. However, Smart Grids also have an impact on the overall EA of grids operators. Therefore, our framework enables stakeholders to validate and criticize their modeling choices for the EA in the context of Smart Grids. What we propose is a multi-view framework with three aspects – information, processes, and goals – for each view. In addition to thebusiness, functional and application views, we add an integration view to ensure inter and intra-view consistency. We rely on Model Driven Engineering (MDE) techniques to ease the holistic modeling and simulation of enterprise systems. Finally, we show the utility of our approach by applying it on a Smart Grid case study: the management of an electric vehicles fleet.