Internet of Things (IoT) devices are devices with embedded sensors or software. Their usage is becoming more prevalent. They are used for smart homes, traffic tracking, weather data and sensor networks. Often IoT devices communicate wirelessly using low-power protocols. A wireless connection provides an easy way to eavesdrop on personal data or even hijack connections. Thus security measures need to be taken. Our thesis looks at a specific IoT operating system, Contiki-NG, and describes the porting process of a Transportation Layer Protocol (TLS) protocol library, MbedTLS. The porting process consists of specific modules that need to be adapted to the Contiki-NG environment. After porting, example programs that send and receive datawere written to enable performance testing that includes execution time, runtime memory usage and code space. The results of the tests were analyzed and evaluated. The produced implementation from porting MbedTLS to Contiki-NG provides a secure connection between two hosts running the TLS protocol. The highly configurable nature of MbedTLS allows the implementation to be adapted to comply to the resource constraints of platforms supported by Contiki-NG. / Internet of Things (IoT)-enheter är enheter med inbyggda sensorer eller mjukvara. Deras användning blir allt vanligare. De används för smarta hem, trafikspårning, väderdata och sensornätverk. Ofta kommunicerar IoT-enheter trådlöst med protokoll med låg energiförbrukning. En trådlös anslutning ger ett enkelt sätt att avlyssna personuppgifter eller till och med att kapa anslutningar. Därför måste säkerhetsåtgärder vidtas. Vårt examensarbete täcker ett specifikt IoT-operativsystem Contiki-NG och beskriver en portningsprocess av ett TLS-protokollbibliotek (Transportation Layer Protocol), MbedTLS. Portningen består av specifika moduler som behöver anpassas till den ursprungliga Contiki-NG-miljön. Efter portningen skrevs exempelprogram som skickar och tar emot data för att möjliggöra prestandatestning som inkluderar exekveringstid, minnesanvändning och kodutrymme. Resultaten av testerna analyserades och utvärderades. Den framställda implementeringen från att överföra MbedTLS till Contiki- NG gav en säker anslutning mellan de två ändpunkterna som kommunicerar med TLS-protokollet. Den i hög grad konfigurerbara karaktären hos MbedTLS gjorde det möjligt att anpassa implementeringen för att följa resursbegränsningarna för plattformar som stöds av Contiki-NG.

El sector automotriz de motocicletas creció sostenidamente dentro del período 2016 al 2018, con crecimientos de ventas acumuladas del 3.42%, mayor al sector de automóviles que decreció en el mismo período. En el marco global existe tendencias de generar eficiencias y ofrecer servicios de valor mediante la digitalización, donde el concepto IoT (Internet de las cosas, siglas en inglés) ofrece soluciones que mejoran experiencias de uso tanto para usuarios finales de motocicletas como para quien las vende. Por ello, se considera usar dispositivos inteligentes, que permiten al usuario ubicar su vehículo, el recorrido histórico, compartimiento de rutas, cortar combustible, entre otros; cubriendo la necesidad de seguridad que requiere alguien que compra una motocicleta, sabiendo que el índice de robo vehicular crece constantemente, y una solución como ésta permite actuar inmediatamente para recuperar el vehículo. En esta situación, es lógico plantear soluciones IoT para que usuarios finales cuenten con alternativas que mejoren su seguridad vehicular, sin embargo, la realidad es que éstas no son tan populares como el mercado indica; debido a fallas de mercado que impiden masificar la venta de motocicletas con esta solución, y sólo está disponible para quienes explícitamente lo soliciten, debido a que la relación valor de la solución frente al valor ticket de motocicleta es alta. Bajo situaciones parecidas, aunque no iguales, el mercado de automóviles tiene normativas que condiciona la venta de principales marcas y modelos al uso de dispositivos inteligentes complementarios al seguro vehicular, y permite recuperar vehículos con menor riesgo ante un robo. / The motorcycle automotive sector grew steadily from 2016 to 2018, with accumulated sales growth of 3.42%, higher than the automobile sector, which decreased in the same period. In the global framework, there are trends to generate efficiencies and offer value services through digitization, where the IoT (Internet of Things) concept offers solutions that improve user experiences both for end users of motorcycles and for those who sell them. Therefore, it is considered to use smart devices, which allow the user to locate their vehicle, the historical route, route sharing, cut fuel, among others; covering the security need required by someone who buys a motorcycle, knowing that the rate of vehicular theft is constantly growing, and a solution like this allows to act immediately to recover the vehicle. In this situation, it is logical to propose IoT solutions so that end users have alternatives that improve their vehicle safety, however, the reality is that these are not as popular as the market indicates; Due to market failures that prevent the mass sale of motorcycles with this solution, and it is only available to those who explicitly request it, because the value of the solution versus the value of the motorcycle ticket is high. Under similar, although not equal, situations, the car market has regulations that condition the sale of main brands and models to the use of intelligent devices complementary to vehicle insurance, and allow to recover vehicles with less risk in the event of theft / Tesis

Detecting anomalies in computer-based systems, including Cyber-Physical Systems (CPS), has attracted a large interest recently. Behavioral anomalies represent deviations from what is regarded as the nominal expected behavior of the system. Both Process science and Data science can yield satisfactory results in detecting behavioral anomalies. Within Process Mining, Conformance Checking addresses data retrieval and the connection of data to behavioral models with the aim to detect behavioral anomalies. Nowadays, computer-based systems are increasingly complex and require appropriate validation, monitoring, and maintenance techniques. Within complex computer-based systems, the European Rail Traffic Management System/European Train Control System (ERTMS/ETCS) represents the specification of a standard Railway System integrating heterogeneous hardware and software components, with the aim of providing international interoperability with trains seemingly interacting within standardized infrastructures. Compliance with the standard as well as expected behavior is essential, considering the criticality of the system in terms of performance, availability, and safety. To that aim, a Process Mining Conformance Checking process can be employed to validate the requirements through run-time model-checking techniques against design-time process models. A Process Mining Conformance Checking methodology has been developed and applied with the goal of validating the behavior exposed by an ERTMS/ETCS system during the execution of specific scenarios. The methodology has been tested and demonstrated correct classification of valid behaviors exposed by the ERTMS/ETCS system prototype. Results also showed that the Fitness metric developed in the methodology allows the detection of latent errors in the system before they can generate any failures.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / The present work proposes an algorithm to detect and identify the artifact signals produced by the concrete gestural actions of jaw clench and eyebrows raising in the electroencephalography (EEG) signal. Artifacts are signals that manifest in the EEG signal but do not come from the brain but from other sources such as flickering, electrical noise, muscle movements, breathing, and heartbeat. The proposed algorithm makes use of concepts and knowledge in the field of signal processing, such as signal energy, zero crossings, and block processing, to correctly classify the aforementioned artifact signals. The algorithm showed a 90% detection accuracy when evaluated in independent ten-second registers in which the gestural events of interest were induced, then the samples were processed, and the detection was performed. The detection and identification of these devices can be used as commands in a brain–computer interface (BCI) of various applications, such as games, control systems of some type of hardware of special benefit for disabled people, such as a chair wheel, a robot or mechanical arm, a computer pointer control interface, an Internet of things (IoT) control or some communication system. / Revisión por pares

La sociedad está siendo testigo de una revolución digital que ha generado cambios en diversos ámbitos, entre los cuales se encuentra la construcción o transformación de edificios comunes a Smart Buildings (SB), lo que puede conseguir que varios aspectos como la seguridad, el consumo energético, entre otros, se gestionen con mayor eficiencia. El desarrollo de SBs es acompañado del desarrollo de las tecnologías IoT (Internet of Things), las cuales son una opción de soporte para convertir un edificio en inteligente. Sin embargo, la implementación de SBs está sujeta a varios riesgos que no se han cubierto en conjunto, destacando tres de ellos que son la incapacidad de escalar el SB para que se adapte a nuevos requisitos (escalabilidad), que los dispositivos IoT (sensores y actuadores) no sean compatibles entre ellos (incompatibilidad), y que la transmisión de datos en el sistema sea vulnerada por agentes maliciosos (seguridad en la transmisión de datos). Siendo así, el presente proyecto propone una arquitectura tecnológica para las implementaciones de SBs con IoT que brinde a las organizaciones un diseño guía que les permita cambiar sus componentes según la complejidad y exigencia de sus requerimientos, y que solucione en conjunto los tres riesgos previamente mencionados. Finalmente, el resultado del proyecto es una arquitectura tecnológica para las implementaciones de SBs validada en el cumplimiento de los requisitos planteados. / Society is witnessing a digital revolution that has generated changes in various areas, among which is the construction or transformation of common buildings to Smart Buildings (SB), which can achieve that various aspects such as security, energy consumption, among other things, are managed more efficiently. The development of SBs is accompanied by the development of IoT (Internet of Things) technologies, which can support the transformation of a regular building into an SB. However, the implementation of SBs is subject to several risks that have not been covered together, highlighting three of them which are the inability to scale the SB to adapt to new requirements (scalability), that IoT devices (sensors and actuators) are not compatible with each other (incompatibility), and that the data transmission inside the system is compromised by malicious agents (security in data transmission). Thus, this project proposes a technological architecture for the implementations of SBs with IoT that provides organizations with a guiding design that allows them to change their components according to the complexity and demand of their requirements, and that jointly solves the three previously mentioned risks. Finally, the result of the project is a technological architecture for the implementations of SBs validated in the fulfillment of the raised requirements. / Tesis

<p>Makerspaces can support educational experiences in prototyping for young learners, while physical computing platforms enable high levels of creativity and expression. However, each of these have high barriers of entry, especially for youth without prior experience. Commercial microcontrollers have become a popular tool to use when designing both basic and complex electro-mechanical devices. Block-based programming interfaces are an ideal environment to program these microcontrollers, lowering the barrier of entry for young, novice programmers. Finally, incorporating physical computing devices alongside Augmented Reality (AR) presents opportunities for shared experiences between users, leading to more engagement, which creates opportunities for social interaction, ideation, and creativity. This thesis presents the MakAR project, which aims to empower youth to design, build, and play through interactions with AR, physical prototyping, and the Internet of Things (IoT). We sought to design an interactive system with which we can (1) explore novel interactions between physical and virtual content, (2) lower the barrier to entry for young makers to design, build, and play with DIY electro-mechanical devices, and (3) engage and collaborate with one another both virtually and physically in an AR-IoT environment. Our results provide evidence to how our system enables youth, with and without prior experience with physical computing, to both engage and struggle with designing AR-IoT interactions while using our system, helps reduce the barrier to entry for young users to create complex electro-mechanical devices, and provides unique shared AR-IoT experiences for both novices and expert users.</p>

Smart technology has become very extensive and popular in many areas of our lives in the last few years. Their role is to make life and activities performed by the user easier, faster, better and more interesting. Smart technology has been used for years in areas such as education, industry and transport. The response of the hospitality industry is to implement technology in hotels, usually the more luxurious ones, that improves the operation of employees and is a convenience for hotel guests. The name of this concept is a smart hotel, which is similar to the already existing smart house and smart city concepts. The purpose of the study was to examine the concept of smart hotel and investigate the impact that smart technology has on guests’ satisfaction, privacy and service quality. The study was conducted using mixed methods research whereby both qualitative and quantitative data were collected in parallel, analyzed individually and then linked with each other. Qualitative data collection constituted of in-dept interviews with hotel employees and hotel guests, and the quantitative part of the study was based on the online survey with hotel guests. The main findings are a proposal definition of a smart hotel, and the belief that smart technology contributes to a better quality of provided services by employees and increases the grade of satisfaction among guests. However, the result also shows that a greater role of hotels is needed in order to reduce the disproportion of equal access to smart technology for all hotel guests.

The current manufacturing business environment becomes more dependent of digital tools to increase business opportunities and customer value. The organizations ability to embrace the digital tools is dependent of a its digital maturity position. The organization structure, information systems, and communication are variables affecting the position and enables or disables possibility of data-based decisions-making (DBDM). To determine the ability information system and information flow is analyzed through a case study at one of the production departments. The areas studied in the case study are information flow of metal powder and compression tools.  The case study is performed to study the organizations ability to connecting information, study information flow and assess potential information disruption. It is assessed by using digital maturity assessments. This result provides an insight of how it affects the DBDM abilities within the department. These areas are common in a general production setting. The metal powder area is analyzed by an experiment where the metal powder containers is manually measured and evaluate the real weight compared to the depreciated weight in the information system. The compression tool analysis is performed by extracting and analyzing structured- and unstructured machine data from the production. This analytical angle is dependent of reliable data, and information disruption between the production processes and the servers is noticed during the extraction of data. This extraction process and analysis resembles the need when implementing machine learning and other automatic applications.  The 360DMA assess a general view of the organizations position and follow up with a method how to reach certain goals to increase one of the five levels. The Acatech-model is used to assess two structural areas, resources and information systems. The metal powder container analysis shows that there is a problem between the information stored in the systems regarding weight of the metal powder containers. The compression tool analysis result is that the stored data about the compression tools and the count of the different components is not correct. This and difficulties with manually- and automatically extracting data from server’s cause information disruptions and decrease the production process information reliability and validity. This decrease the ability to use the production data to make data driven decisions and gain insights about the production. The digital maturity assessment position the organization on a connectivity level (Acatech model) regarding information systems and resources means that data is unreliable and once its reliable the next level is in reach. The varying position within the 360DMA model call for management to synchronize development between processes by introducing strategies, define responsibilities and understand the information flow.

The new reality is about connectivity. From social media to collaborative tools in work environments, people and their devices are highly intercommunicated. This reality also affects the manufacturing industry, which has been developing standards to keep up with technology and take advantage of it. These technologies are encompassed in what has been called Industry 4.0. Out of all the technologies, apart from artificial intelligence, the main drives of Industry 4.0 are considered by many studies augmented reality (AR) and industrial internet of things (IIoT). This thesis suggests that these drivers have been mostly tested either in an isolated fashion or with limited connectivity. The main objective is to explore the advantages of connecting AR, IIoT, and the main source of information in the manufacturing process: product lifecycle management (PLM) systems. Two sub-objectives are extracted from the main one: to study of value created from real-time role-dependent AR information displaying, and to study how the connection AR-IIoT-PLM can affect developing and deploying times of AR experiences. To achieve the mentioned objectives, a case of study is built, and the combined system implemented on it. Following design and creation methodology, an artefact is created, and conclusions are extracted from the design, implementation, and usage stages. Main results are an improvement in developing and deploying time of AR experiences, along with guidelines to extract as much value as possible from the implementation of a combined system of this sort. This thesis includes a discussion section, where the potential of the system is explained and framed within the near future of manufacturing companies.

As more IoT devices enter the market it becomes increasingly important to develop reliable and adaptive ways of dealing with the data they generate. These must address data quality and reliability. Such solutions could benefit both the device producers and their customers who, as a result, could receive faster and better customer support services. Thus, this project's goal is twofold. First, it is to identify faulty data points generated by such devices. Second, it is to evaluate whether the knowledge gained from available/known sensors and appliances is transferable to other sensors on similar devices. This would make it possible to evaluate the behaviour of new appliances as soon as they are first switched on, rather than after sufficient data from them has been collected. This project uses time series data from three appliances: washing machine, washer&amp;dryer and refrigerator. For these, two solutions are developed and tested: one for categorical and another for numerical variables. Categorical variables are analysed using the Average Value Frequency and the pure frequency of state-transition methods. Due to the limited number of possible states, the pure frequency proves to be the better solution, and the knowledge gained is transferred from the source device to the target one, with moderate success. Numerical variables are analysed using a One-class Support Vector Machine pipeline, with very promising results. Further, learning and forgetting mechanisms are developed to allow for the pipelines to adapt to changes in appliance patterns of behaviour. This includes a decay function for the numerical variables solution. Interestingly, the different weights for the source and target have little to no impact on the quality of the classification. / Nya IoT-enheter träder in på marknaden så det blir allt viktigare att utveckla tillförlitliga och anpassningsbara sätt att hantera de data de genererar. Dessa bör hantera datakvalitet och tillförlitlig- het. Sådana lösningar kan gynna båda tillverkarna av apparater och deras kunder som som ett resultat kan dra nytta av snabbare och bättre kundsupport / tjänster. Således har detta projekt två mål. Det första är att identifiera felaktiga datapunkter som genereras av sådana enheter. För det andra är det att utvärdera om kunskapen från tillgängliga / kända sensorer och apparater kan överföras till andra sensorer på liknande enheter. Detta skulle göra det möjligt att utvärdera beteendet hos nya apparater så snart de slås på första gången, snarare än efter att tillräcklig information från dem har samlats in. Detta projekt använder tidsseriedata från tre apparater: tvättmaskin, tvättmaskin och torktumlare och kylskåp. För dessa utvecklas och testas två lösningar: en för kategoriska variabler och en annan för numeriska variabler. De kategoriska variablerna analyseras med två metoder: Average Value Frequency och den rena frekvensen för tillståndsövergång. På grund av det begränsade antalet möjliga tillstånd visar sig den rena frekvensen vara den bättre lösningen, och kunskapen som erhålls överförs från källanordningen till målet, med måttlig framgång. De numeriska variablerna analyseras med hjälp av en One-class Support Vector Machine-pipeline, med mycket lovande resultat. Vidare utvecklas inlärnings- och glömningsmekanismer för att möjliggöra för rörledningarna att anpassa sig till förändringar i apparatens beteendemönster. Detta inkluderar en sönderfallningsfunktion för den numeriska variabellösningen. Intressant är att de olika vikterna för källan och målet har liten eller ingen inverkan på kvaliteten på klassificeringen.