Background: Internet of Things is one of the most important areas of future technology and countless of industries are directing attention towards it. IoT has now started to appear in the sport industry. One sport that has not been investigated to the same extent within the terms of IoT is ice hockey. Problem Statement: Numerous teams in ice hockey have not yet realized the impact IoT may have on their team performance. With an absence in research on the use of IoT in the Swedish ice hockey industry, there is a knowledge gap on how Swedish ice hockey teams can grasp this opportunity and the main factors that affect their adoption. Research Purpose: IoT creates an opportunity for Swedish ice hockey teams to achieve a competitive advantage and thus a chance to gain new grounds in managing their teams. The purpose of this thesis is to investigate to what extent teams in the Swedish Hockey League are using IoT devices and explore the factors affecting the adoption process. Research Questions: To what extent are IoT devices used by teams in the Swedish Hockey League to increase teams’ performance? What main factors affect the adoption of IoT devices into teams in the Swedish Hockey League? Method: With an inductive approach, this qualitative research explores the IoT phenomenon in the context of ice hockey in SHL. With semi-structured interviews, this research gather data from the perspectives of seven SHL teams on IoT. By using a conventional content analysis, the data collected is categorized and divided into themes. Results: The use of IoT devices in SHL is low. The interest to adopt IoT devices is shared amongst many interview participants. When conducting the conventional content analysis on the data gathered from the interviews, certain themes became evident. The findings could be traced down to either their attitude, their competence within the field or their resources. Conclusion: The Internet of Things is changing the way professional sport teams are managed, coached, and led. The benefits that could be harvested from adopting IoT devices are undeniable, but there are several factors that facilitate a successful adoption. The culture and atmosphere in the organization, the skills and know-how, and the financial situation are all important parts of a successful adoption.

The growth of connected, or so called smart home appliances, leads to the search for interaction methods for IoT devices including various forms of smartphone app solutions. Previous research has investigated how complex systems, with multiple options and configurations, can be set up through end-user programming and how app widgets can be an interactive solution. This approach has been adopted by few providers, however, there is little feedback from users on their experiences, in particular on the ease of configuration and customization of app widgets, the ease of use and expectations. To address these questions, I developed a novel app widget as a complement to an existing IoT smartphone app, along with three evaluations: one in-volving 8 experts including designers, developers and product managers; followed by a test session with 10 end users; and finally a field trial in which 3 users lived with the ability to use the widget for two weeks. Feedback and insights about how app widgets affected the user experience were gathered during the process confirming that widgets is a promising solution for controlling smart devices and, depending on the scenario and type of users, there are considerations that could limit or enhance their functionality. / Framväxten av sammankopplade hushållsapparater, eller så kallade ”smart home appliances”,aktualiserar sökandet efter interaktionsmetoder för IoT-produkter, t.ex. smartphone-appar. Tidigare forskning har undersökt hur komplexa system, med flera alternativ och konfigurationer, kan anpassas genom ”end-user-programmering”, och hur app-widgetar kan möjliggöra interaktiva lösningar. Ett fåtal leverantörer har annamat dessa tillvägagångssätt, men det saknas fortfarandefeedback fån användare, särskilt gällande konfigurering och specialanpassning av app-widgetar, lättanvändhet, och förväntningar. För att ta itu med dessa frågor utvecklade jag en ny app-widget som komplement till en etablerad IoT-app för smartphones, samt tre utvärderingar: en med åtta experter, inklusive designers, produktutvecklare, och produktchefer; följt av ett test med tio slutanvändare; och slutligen ett fältförsök där tre anvädare kunda använda app-widgeten i hemmet under två veckor. Under processen samlades feedback och insikter om app-widgetars inverkan på användarupplevelsen, vilka bekräftade att app-widgetar är en lovande metod för att kontrollera ”smart home appliances” och att det, beroende på scenario och användartyp, finns säskilda punkter som kan begränsa eller förhöja deras användbarhet. Framväxten av samman-kopplade hushållsapparater, eller så kallade ”smart home appliances”, aktualiserar sökandet efterinteraktionsmetoder för IoT-produkter, t.ex. smartphone-appar. Tidigare forskning har undersökt hur komplexa system, med flera alternativ och konfigurationer, kan anpassas genom ”end-user-programmering”, och hur app-widgetar kan möjliggöra interaktiva lösningar. Ett fåtal leverantörer har annamat dessa tillvägagångssätt, men det saknas fortfarande feedback från användare, särskilt gällande konfigurering och specialanpassning av app-widgetar, lättanvändhet, och förväntningar. För att ta itu med dessa frågor utvecklade jag en ny app-widget som komplement till en etablerad IoT-app för smartphones, samt tre utvärderingar: en med åtta experter, inklusive designers ,produktutvecklare, och produktchefer; följt av ett test med tio slutanvändare; och slutligen ett fältförsök där tre anvädare kunde använda app-widgeten i hemmet under två veckor. Under processen samlades feedback och insikter om app-widgetars inverkan på användarupplevelsen, vilka bekräftade att app-widgetar är en lovande metod för att kontrollera ”smart home appliances” och att det, beroende på scenario och användartyp, finns säskilda punkter som kan begränsa eller förhöja deras användbarhet.

The Internet of Things (IoT) is an infrastructure of physical objects connected via the Internet that can exchange data to achieve efficient resource management. Billions of devices must be self-powered and low-cost considering the massive scale of the IoT. Thus, there is a need for low-cost ambient energy harvesters to power IoT devices. It is a challenging task since ambient energy might be unpredictable, intermittent and insufficient. For example, solar energy has limitations such as intermittence and unpredictability despite utilizing the highest power availability and relatively mature technology. Designing a multi-source energy harvester (MSEH) based on continuous and ubiquitous ambient energy sources might alleviate these issues by providing versatility and robustness of power supply. However, combining several energy harvesters into one module must be done synergistically to ensure miniaturization, compactness and more collected energy. Also, additive manufacturing techniques must be used to achieve low-cost harvesters and mass manufacturability. This dissertation presents two different kind of ambient energy harvesters, namely radio frequency energy harvester (RFEH) and thermal energy harvester (TEH). Each harvester is individually optimized and then synergistically combined into a MSEH. First, RFEH is designed for triple-band harvesting (GSM900, GSM1800, 3G2100) using the antenna-on-package concept and fabricated through 3D and screen printing. TEH collects energy from temperature fluctuations of ambient environment through a combination of thermoelectric generators and phase change materials. It is adapted specifically for the desert conditions of Saudi Arabia. Later, TEH and RFEH are combined to realize MSEH. Smart integration is achieved by designing a dual-function component, heatsink antenna, that serves as a receiving antenna of RFEH and a heatsink of TEH. The heatsink antenna has been optimized for both antenna radiation performance and heat transfer performance. Field tests showed that the MSEH can collect 3680μWh energy per day and the outputs of TEH and RFEH have increased 4 and 3 times compared to the independent TEH and RFEH respectively. To validate the utility of the MSEH, a temperature/humidity sensor has been successfully powered by the MSEH. Overall, sensor’s data can be wirelessly transmitted with time intervals of 3.5s, highlighting the effectiveness of the synergistic MSEH.

Ambient temperature sensing plays an important role in a number of applications in agriculture, industry, daily health care. In this thesis project, we propose a new acoustic-based ambient temperature sensing method called Mtemp. Mtemp empowers acoustic-enabled IoT devices, smartphones to perform ambient air temperature sensing without additional hardware. Basically, Mtemp utilizes on-board speaker and microphone to calculate the propagation speed of acoustic signal by measuring the phrase of the target signal, thereby estimate the ambient temperature according to a roughly linear relationship between temperature and sound speed. Mtemp is portable and economical, making it competitive compared with traditional thermometers for ubiquitous sensing.

The Internet of Things (IoT) is a concept that is getting a lot of attention. IoT devices are growing in popularity and so is the need to protect these devices from attacks and vulnerabilities. Future developers and users of IoT devices need to know what type of devices need extra care and which are more likely to be vulnerable. Therefore this study has researched the correlations between combinations of protocols and software vulnerabilities. Fifteen protocols used by common services over the internet were selected to base the study around. Then an artificial neural network was used to group the devices into 4 groups based on which of these fifteen protocols were running. Publicly disclosed vulnerabilities were then enumerated for all devices in each group. It was found that the percentage of vulnerable devices in each group differed meaning there is some correlation between running combinations of protocols and how likely a device is vulnerable. The severity of the vulnerabilities in the vulnerable devices were also analyzed but no correlation was found between the groups. / Sakernas internet eller Internet of things (IoT) är ett koncept som fått mycket uppmärksamhet. IoT enheter växer drastisk i popularitet, därför är det mer nödvändigt att skydda dessa enheter från attacker och säkerhetsbrister. Framtida utvecklare och användare av IoT system behöver då veta vilka enheter som är mer troliga att ha säkerhetsbrister. Denna studie har utforskat om det finns något samband mellan kombinationer av aktiva protokoll i enheter och säkerhetsbrister. Femton vanligt använda protokoll valdes som bas för studien, ett artificiellt neuralt nätverk användes sedan för att gruppera enheter baserat på dessa protokoll. Kända sårbarheter i enheterna räknades upp för varje grupp. En korrelation mellan kombinationer av protokoll och trolighet för sårbarheter hittades. Allvarlighetsgraden av säkerhetsbristerna i sårbara enheter analyserades också, men ingen korrelation hittades mellan grupperna.

This thesis describes energy consumption and power supplying low-power IoT devices. There is general analysis of current consumption and selecting suitable primary battery cell depends of behavior battery type. The next point is analysis of low-power and high-effectivity DC/DC convertor’s feature. The second part contains available solutions for energy monitoring and current measurement. This thesis also contains design of device for measure these energy consumption, with user application, which interprets measured data.

IoT-enheter är för många människor idag en del av vardagen och fler och fler enheter ansluts till människors hushåll. Smarta hem har allt från kylskåp och övervakningskameror som är anslutna till ett nätverk och internet. Problematiken med det är att många av dessa enheter har inte tillräcklig kapacitet eller en avsaknad av tillräckliga säkerhetsåtgärder för att skydda sig mot potentiella attackvektorer. Bristande säkerheten för enheter i det smarta hemmet kan leda till att enheterna blir hackade och manipulerade av angripare. Den som kan skydda det smarta hemmet mot manipulation av dess IoT-enheter är både användare i det smarta hemmet och utvecklare av enheterna. Men det är inte alltid tydligt för vem åtgärden gäller, vilket är något den här studien vill klargöra. Den här litteraturstudien utgår från befintliga åtgärder identifierade av tidigare forskning. Den skiljer sig från den tidigare forskningen genom att kartlägga vilka åtgärder som är applicerbara för användare och utvecklare för att skydda det smarta hemmet mot manipulation. Med hjälp av en systematisk litteratursökning valdes 22 studier ut för att besvara studiens frågeställning. För att besvara studiens forskningsfråga används en kartläggande granskningsmetod. Metoden används för att kartlägga och identifiera vilka åtgärder som rekommenderas för både användare och utvecklare. Med hjälp av den tidigare forskningen framställs ett ramverk för att förtydliga vad användaren i det smarta hemmet själv utföra och vilka åtgärder utvecklare kan och bör utföra. Ramverket demonstrerar dessutom en rekommenderad ordning från författaren som åtgärderna bör utföras i. / Users of IoT devices are for many people today part of everyday life and more and more devices are connected to people's households. Smart homes have everything from refrigerators and surveillance cameras connected to a network and the internet. The problem with this is that many of these units do not have sufficient capacity or lack adequate security measures to protect themselves against potential attack vectors. Lack of security for devices in the smart home can cause the devices to be hacked and manipulated by attackers. Those who can protect the smart home from tampering with its IoT devices are the users in the smart home and the developers of the IoT devices. Although it is not always clear for whom the security measures apply to, which is something that this study aims to clarify. This literature study is based on existing security measures identified by previous research. It differs from previous research by mapping out which security measures and best practices that are applicable to users and developers to protect the smart home from being manipulated. Using a systematic literature search, 22 studies were selected to answer the study's question. To answer the study's research question, a mapping method is used. The method is used to map and identify which actions are recommended for both users and developers. Using the previous research, a framework is created to clarify what the user in the smart home can do and what actions developers can and should take. The framework also demonstrates a recommended order from the author in which the measures should be carried out.

Smart wearables, known as emerging paradigms for vital big data capturing, have been attracting intensive attentions. However, one crucial problem is their power-hungriness, i.e., the continuous data streaming consumes energy dramatically and requires devices to be frequently charged. Targeting this obstacle, we propose to investigate the biodynamic patterns in the data and design a data-driven approach for intelligent data compression. We leverage Deep Learning (DL), more specifically, Convolutional Autoencoder (CAE), to learn a sparse representation of the vital big data. The minimized energy need, even taking into consideration the CAE-induced overhead, is tremendously lower than the original energy need. Further, compared with state-of-the-art wavelet compression-based method, our method can compress the data with a dramatically lower error for a similar energy budget. Our experiments and the validated approach are expected to boost the energy efficiency of wearables, and thus greatly advance ubiquitous big data applications in era of smart health.<br><div>In recent years, there has also been a growing interest in edge intelligence for emerging instantaneous big data inference. However, the inference algorithms, especially deep learning, usually require heavy computation requirements, thereby greatly limiting their deployment on the edge. We take special interest in the smart health wearable big data mining and inference. <br></div><div><br></div><div>Targeting the deep learning’s high computational complexity and large memory and energy requirements, new approaches are urged to make the deep learning algorithms ultra-efficient for wearable big data analysis. We propose to leverage knowledge distillation to achieve an ultra-efficient edge-deployable deep learning model. More specifically, through transferring the knowledge from a teacher model to the on-edge student model, the soft target distribution of the teacher model can be effectively learned by the student model. Besides, we propose to further introduce adversarial robustness to the student model, by stimulating the student model to correctly identify inputs that have adversarial perturbation. Experiments demonstrate that the knowledge distillation student model has comparable performance to the heavy teacher model but owns a substantially smaller model size. With adversarial learning, the student model has effectively preserved its robustness. In such a way, we have demonstrated the framework with knowledge distillation and adversarial learning can, not only advance ultra-efficient edge inference, but also preserve the robustness facing the perturbed input.</div>

The trending usage of IoT devices raises serious security concerns. IoT devices have complete access to users’ network environments. In the eyes of hackers, the value of IoT devices is exceptionally high. From minor disturbances to major crimes, all could happen in no time with compromised IoT devices. As the IoT devices collects sensitive data, properly protect users’ privacy is also a crucial aspect for IoT devices. Thus, IoT devices need to be secure enough against modern cyber-attacks. In this work, a smart camera DCS-8515LH from D-Link is under penetration tests. Threat modeling is first performed as an analysis of the IoT system following by a dozen cyber attacks targeting this smart camera. The penetration tests provide valuable information that can reveal the smart camera’s vulnerability and weakness, such as security misconfiguration, vulnerability to DoS attacks. The smart camera is discovered to be vulnerable to DoS attacks and exploits on the zero-configuration protocol. Several weaknesses which violate the users’ privacy exist in the mobile application and Android storage system. This work evaluated all the vulnerabilities and weaknesses discovered from a security aspect. This report exposes attacks that are effective on the smart camera and also serves as a fundamental basis for future penetration tests on this smart camera. / I detta arbete är en smart kamera DCS-8515LH från D-Link under penetrationstester. Hotmodellering utförs först som en analys av IoT-systemet följt av ett dussin cyberattacker riktade mot denna smarta kamera. Penetrationstesterna ger värdefull information som kan avslöja den smarta kamerans sårbarhet och svaghet, såsom säkerhetsfelkonfiguration, sårbarhet för Dos-attacker. Den smarta kameran har upptäckts vara sårbar för DoS-attacker och utnyttjande av nollkonfigurationsprotokollet. Flera svagheter som kränker användarnas integritet finns i mobilapplikationen och Android-lagringssystemet. Detta arbete utvärderade alla sårbarheter och svagheter som upptäckts ur en säkerhetsaspekt. Den här rapporten avslöjar attacker som är effektiva på den smarta kameran och fungerar också som en grundläggande bas för framtida penetrationstester på denna smarta kamera.

Under de senaste åren har stora datamängder blivit allt vanligare för beslutsfattande och analys. Maskininlärning och matematiska beräkningar är två avgörande metoder som används för detta. Dessa beräkningar kan dock vara tidskrävande, och de kräver högpresterande datorer som är utmanande att skala upp. Raspberry Pi är en liten, kraftfull och billig dator som lämpar sig för parallella beräkningar. Kubernetes är en öppen källkodsplattform för att hantera containerbaserade applikationer som tillåter automatisk skalning av mjukvaruapplikationer. Genom att kombinera Raspberry Pi med Kubernetes kan ett kostnadseffektivt och skalbart system för matematiska beräkningar och maskininlärning skapas. I denna studie undersöks möjligheten att bygga ett kostnadseffektivt och skalbart system för matematiska beräkningar och maskininlärning med hjälp av Raspberry Pi och Kubernetes. Det kommer att göras teoretisk forskning kring Kubernetes och Raspberry Pi, designa ett system för matematiska beräkningar och maskininlärning, implementera systemet genom att installera och konfigurera Kubernetes på flera Raspberry Pi:s, mäta och utvärdera systemets prestanda och skalbarhet samt presentera studiens resultat. Resultatet visade att användningen av Raspberry Pi i kombination med Kubernetes för att utföra matematiska beräkningar är både kostnadseffektiv och skalbar. När det gäller prestanda kunde systemet hantera intensiva beräkningsuppgifter på ett tillfredsställande sätt, vilket visar sin potential som en lösning för storskalig dataanalys. Förbättringar i systemdesign och mjukvaruoptimering kan ytterligare öka effektiviteten och prestanda / In the recent years, large data sets have become more often used for decision-making and analysis. Machine learning and mathematical calculations are two crucial methods employed for this. However, these computations may be time-consuming, and they require highperformance computers that are challenging to scale up. Raspberry Pi is a small, powerful, and cheap computer suitable for parallel calculations. Kubernetes is an open-source platform for managing container-based applications that allows automatic scaling of software applications. By combining Raspberry Pi with Kubernetes, a cost-effective and scalable system for mathematical calculations and machine learning can be created. In this study, the possibility of building a cost-effective and scalable system for mathematical calculations and machine learning using Raspberry Pi and Kubernetes is investigated. There will be theoretical research on Kubernetes and Raspberry Pi, design a system for mathematical calculations and machine learning, implement the system by installing and configuring Kubernetes on multiple Raspberry Pi's, measure and evaluate the system's performance and scalability, and present the study's results. The result showed that the use of Raspberry Pi in combination with Kubernetes to perform mathematical calculations is both cost-effective and scalable. In terms of performance, the system was able to handle intensive computational tasks satisfactorily, demonstrating its potential as a solution for large-scale data analysis. Improvements in system design and software optimization can further increase efficiency and performance.