The number of connected Internet of Things devices are expected to surpass 30 billion in 2020. The unprecedented levels of personal data sharing are drastically increasing the complexity of privacy challenges. This kindled efforts such as the General Data Protection Regulation (GDPR), that came into effect in May 2018 to establish user data rights. These new user data rights have had a considerable impact both for the users, and the data controllers & third-parties that are liable to effectuate the new requirements, such as privacy-by-design and explicit consent. In this thesis, we explore this impact of the GDPR, specifically on self-disclosure of personal data through smart home devices, in order to gain insights for smart home practitioners. In doing so, we specifically want to answer two research questions. Our first research question helps us understand opinions and attitudes, specifically those of Swedish residents. An online survey helps us understand their willingness and fears of adopting smart home devices. In our second research question, we apply a semi-Systematic Literature Review to study how the GDPR has influenced self-disclosure through smart home devices, and which factors have had the most significant effect on its users. The survey (n=131) showed that while trust towards data controllers is the cumulatively highest priority of users, consistent product & service quality was more likely to be the first priority (28%). Some users are struggling to find usefulness for smart home devices, so the perceived benefit is currently mainly exceeding the cost and perceived risk for lead adopters. Since the GDPR came into effect, we have seen a raise in user awareness and perceived control. Notably, this led to increased skepticism towards smart home devices. The literature review showed promise in systems to help negotiate and suggest privacy preferences between users and data controllers. We also found an exacerbation of the concern for information privacy and that trust is a major factor for users when deciding to adopt smart home devices. We conclude that there are some factors that are more important than others, as well as provide insights for smart home practitioners on future venues for research and prototyping.

Digitaliseringen av hemmet gör att autonoma enheter blir allt mer vanliga i våra vardagliga liv. Hemmet, som tidigare har varit en privat och personlig plats, blir nu ett centrum för digitala enheter vilket i sig skapar nya teknologiska möjligheter. Det smarta hemmet är en av dessa möjligheter då det erbjuder användaren trygghet och säkerhet samtidigt som enheterna i hemmet kan hjälpa oss med både dagliga problem och långsiktiga utmaningar. En autonom enhet i hemmet kan göra allt ifrån att styra musik till att hålla koll på energiförbrukning. Den gemensamma nämnaren de autonoma enheterna har är att människor tar in dem i sina hem; deras privata sfär, därför blir frågan vi ställer oss följande: Hur bygger en användare tillit mot autonoma enheter i en hemmiljö? / The digitization of the home makes automated systems more common in our everyday lives. The home, which used to be a private and personal place, now becomes a center for digital devices, which in itself creates new technological opportunities. The smart home is one of these opportunities as it offers the user safety and security while the units in the home can help us with both daily problems and long-term challenges. An autonomous unit in a home environment can do everything from controlling music to keeping track of energy consumption. The common denominator the autonomous units have is that people take them into their homes; their private sphere, therefore, the question we ask ourselves becomes the following: How does a user build trust against autonomous units in a home environment?

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.

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The combination of machine learning on embedded systems has quickly increased throughout the years. Subsets like TinyML have become an integral part of how embedded systems implement machine learning. The field has evolved quickly, and TinyOL is an emerging subset that redefines what is possible with embedded systems. This report presents a comparison of how a neural network that implements incremental online learning learns and adapts how to do simple tasks in home automation. The comparison is done with another system, mainly a proportional-integral-derivative (PID). The systems were tasked with controlling an LED lightning threshold based on feedback from the user. The systems were evaluated based on their mean absolute error (MAE) and accuracy in predicting the output of the LED lighting system. The MAE values of both systems were compared for different target outputs and threshold values, and the accuracy was calculated by comparing the number of successful iterations to the total number of iterations. The results show that the neural network has an accuracy of 50\% when a learning rate of 0.2 is used, 97.5\% when a learning rate of 0.5 is used, and 47.5\% when a learning rate of 1.0 is used. The PID control system had accuracy values of 45\% when using an adaption rate of 0.2, 47.5\% when using an adaption rate of 0.5, and 90\% when using an adaption rate of 1.0. The neural network also showcased a lower median MAE for every test conducted. The study provides insights into the effectiveness of different control systems and can inform the development of similar systems in the future. / Kombinationen av maskininlärning på inbygga system har snabbt ökat under åren. Tekniker som TinyML har snabbt blivit en integrerad del av hur inbyggda system implementerar maskininlärning. Teknikerna har snabbt utvecklats och TinyOL är en framväxande delmängd av TinyML som omdefinierar vad som är möjligt med inbyggda system. Denna rapport presenterar en jämförelse av hur ett neuralt nätverk som implementerar inkrementell online-inlärning lär och anpassar sig för att utföra enklare uppgifter inom hemautomation. Jämförelsen görs med ett annat system, huvudsakligen en proportional-integral-derivative (PID). Systemen fick i uppgift att kontrollera en LED-lampa baserat på användarens feedback. Systemet utvärderas baserat på deras mean absolute error (MAE) och noggrannhet i att förutsäga börvärdet för LED-belysningen. MAE-värderna för båda systemen jämfördes för de olika målen och börvärdena, och noggrannheten beräknades genom att jämföra antalet lyckade iterationer med det totala antalet iterationer. Resultaten visar att neurala nätverket har en  noggrannhet på  50\% när en learning rate på 0.2 användes, 97.5\% när en learning rate på 0.5 användes och 47.5\% när en learning rate på 1.0 användes. PID kontroll system hade en noggranhet på 45\% när en adaption rate på 0.2 användes, 47.5\% när en adaption rate på 0.5 användes och 90\% när en adaption rate på 1.0 användes. Det neurala nätverket visade också ett lägre MAE-värde på alla de testerna som utfördes. Studien ger insikter i effektiviteten hos olika kontrollsystem och kan hjälpa utvecklingen av liknande system i framtiden.

Internet of Things (IoT) platforms enable users to connect heterogeneous resource-constrained wireless devices and deploy semantics-rich home automation applications in smart homes. With the proliferation and fragmentation of IoT products, it even becomes a status quo that many smart IoT devices in a smart home are bounded or delegated to multiple IoT platforms, demonstrating the following characteristics. First, an IoT device may interact with more than one IoT platform. Second, a device can be accessed via multiple communication channels (e.g., Zigbee and Bluetooth). Third, automation applications or rules in a smart home may scatter on different platforms, mainly because each platform has its own strengths and restrictions on the supported IoT devices (e.g., many Google Nest devices are not compatible to work with the Amazon platform) and programming features. Despite the convenience brought by the modern platform-backed systems, a variety of attacks targeting the devices, applications and their interactions arise, which can put the smart home devices to insecure, unsafe, or unexpected states, or can breach users' private information in the daily life. In this dissertation, several semantics-aware security and privacy issues are discussed, analyzed and handled. In one way, we systematically categorized a family of cross-app interference threats and designed a Satisfiability modulo theories (SMT)-based solution to detect them. In another work, we revealed a family of delay-based automation interference attacks that cause cross-rule interference problems in multi-platform multi-path systems. We also studied privacy protection of IoT systems and proposed a semantics-aware customizable data flow control system to protect IoT data from being exploited by third parties and attackers. We also established a command sanitizer solution for detecting cross-app interference problems in multi-platform systems and enforce security and safety policies against various IoT threats. All the works were evaluated in real-world smart home testbeds. / Computer and Information Science

The number of IoT (Internet of Things) devices available on the market has been growing rapidly in the past few years and is expected to grow even more in the years to come. These IoT devices are predominantly in the form of very small wireless peripherals with low power consumption making them suitable for running over extended periods of time using only coin cell batteries. In this degree project, conducted at Shortcut Labs AB, we investigate whether or not some of these devises could be suitable for being powered exclusively by kinetic energy without the need for any long term interim power storage, such as batteries or super capacitors. If this is possible it would not only remove the hassle of having to replace batteries at regular intervals, which is important if the devices are positioned at remote locations, but it could also help to reduce the amount of battery waste in the long run. For the sake of this project we have designed a hardware circuit that is able to communicate with other devices using a custom built protocol running on top of the Bluetooth Low Energy standard. This circuit does not require a battery and could potentially be used for many years without the need for any maintenance. To demonstrate this, the technology has successfully been applied to a concept product in the form of a dimmer wheel that can be used to change the brightness or color of Smart Home light bulbs. This is achieved by using a small electric motor as a generator in combination with an energy harvesting circuit in order to generate a stable voltage suitable for use with a wireless module. / Antalet uppkopplade IoT-enheter har ökat drastiskt de senaste åren och väntas fortsätta öka framöver. IoT, eller Sakernas Internet som det kallas på svenska, består övervägande av små trådlösa enheter med så pass låg strömförbrukning att de ofta kan drivas enbart av knappcellsbatterier. I detta examensarbete, utfört på Shortcut Labs AB, undersöker vi huruvida några av dessa enheter med fördel skulle kunna drivas uteslutande av rörelseenergi utan att kräva någon form av långtidsmellanlalgring av denna energi, så som exempelvis i ett batteri eller en kondensator. Om detta var möjligt så skulle det innebära att man slipper byta batterier vid jämna mellanrum, vilket kan vara viktigt om enheten i fråga är otillgänglig placerat. Givetvis kan också onödigt batteriavfall undvikas, något som alltid är eftertraktat i branschen. I detta projekt så har vi designat och konstruerat en elektronikkrets som trådlöst kan kommunicera med andra enheter via ett skräddarsytt protokoll som är implementerat ovanpå Bluetooth Low Energstandarden. Denna krets kräver inget batteri och skulle potentiellt sett kunna operera under många år utan behov av underhåll. För att demonstrera detta så har tekniken applicerats på en konceptprodukt i form av en dimmer som kan användas för att ändra antingen ljusstyrkan eller färgen hos så kallade smarta lampor. Detta uppnås genom att använda en liten DC-motor kombinerad med en energiskördande krets som genererar en lämplig stabil spänning, vilket krävs för att kretsen skall kunna operera.

Background. Smart door locks are either replacing or adding on to more regular mechanical locks today. They come in many different sizes and with many different functionalities. Today, most consumer advertisements focus on the functionalities of the locks and not so much on security. A few years ago when the locks started first to appear, they were very easily hacked and dedicated hackers could ride along the road and unlock people's doors with ease. As this is not the case as it was then, the problem persists. Insecure locks exist but consumer media focuses only on functionality, how are consumers making their purchases? In blind faith or with educated guesses? Objectives. This thesis tends to find out if consumers research the security of a smart lock, how do they research it, and is it enough for them? What sources do they consider before purchasing a lock and do they trust the sources? Can an application with security as its main focus aid or change consumers' methods of research? Methods. To begin answering these questions knowledge of the subject is necessary. Therefore, a literature study was conducted in order to find out what aspects of a smart door lock are more or less secure. To answer the first research question a questionnaire was created to collect quantitative data. This questionnaire contained questions about the participants' knowledge of the subject. Then, interviews were held in order to gather qualitative data for answering the second research question. During the interviews, participants got to test a prototype application helping them understand the subject of the questions. Results. The result shows that a majority of the participants are conscious of security regarding smart locks, 59.8% even think it is the most important aspect. The results from the interviews show that people do want an application suggesting smart locks with security in mind. Conclusions. The research here shows that smart door lock consumers value security. Consumers are also aware that consumer media does not value it as much. However, what is left unanswered is if the consumers care enough to do their own research before purchasing a smart door lock?

Utilizing the ZigBee protocol is pervasive in the context of smart homes, offering substantial convenience to individuals. However, smart home devices commonly handle significant quantities of real-world information, potentially giving rise to concerns related to information leakage. Therefore, in this study, we assess the security of a ZigBee smart homenetworkbyidentifying potential vulnerabilities and conducting a penetration test on the network. In addition, this study compared the potential damage inflicted on the ZigBee network bytechnical and non-technical users. Identifying the potential vulnerabilities was carried out by following a systematic literature review approach, while the penetration testing method was conducted with the help of a case study. The smart home network consisted of a gateway, a smart light bulb, a temperature and humidity sensor, and a motion sensor. The results show a vulnerability in the ZigBee protocol where the technical user could compromise all the security keys. However, the non-technical user was not able to compromise the ZigBee network. Consequently, the security of ZigBee-based smart devices still needs to be further investigated and strengthened. Finally, we discussed the future of the ZigBee network and the optimal scenarios for deploying it.

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