With the development of the Internet-of-Things, more and more IoT platforms come up with different structures and characteristics. Making balance of their advantages and disadvantages, we should choose the suitable platform in differ- ent scenarios. For this project, I make comparison of a cloud-based centralized platform, Microsoft Azure IoT hub and a fully distributed platform, Sensi- bleThings. Quantitative comparison is made for performance by 2 scenarios, messages sending speed adds up, devices lie in different location. General com- parison is made for security, utilization and the storage. Finally I draw the con- clusion that SensibleThings performs more stable when a lot of messages push- es to the platform. Microsoft Azure has better geographic expansion. For gener- al comparison, Microsoft Azure IoT hub has better security. The requirement of local device for Microsoft Azure IoT hub is lower than SensibleThings. The SensibleThings are open source and free while Microsoft Azure follow the con- cept “pay as you go” with many throttling limitations for different editions. Microsoft is more user-friendly.

IoT (Internet of Things) are devices which are usually equipped with different kinds of sensors, these sensor values are then sent to a server where these values are somehow processed. It’s predicted that the usage of IoT devices will increase significantly during the upcoming years.   Microsoft has a service named Azure IoT Hub, which is designed for easy administration of IoT devices, but Azure IoT Hub provides no user interface to administer these IoT devices. The task of this project is to create a user interface for Azure IoT Hub. / IoT (Internet of Things) är enheter som vanligtvis är utrustade med olika slags sensorer, dessa sensorvärden skickas sedan till en server där dessa värden på något sätt bearbetas. Det förutspås att användandet av IoT enheter kommer att öka signifikant under de kommande åren.   Microsoft har en tjänst som heter Azure IoT Hub, vilket är designat för att enkelt administrera IoT enheter, men Azure IoT Hub tillhandahåller inget gränssnitt för att kunna administrera dessa IoT enheter. Uppdraget för detta projekt var därför att skapa ett gränssnitt för Azure IoT Hub.

The Internet of Things (IoT) is rapidly gaining ground in our daily life. There is a trend that the number of devices and data increases rapidly in the future. Meanwhile, IoT platforms are emerging to enable people conveniently deal with the IoT and these huge amount of data and devices. Therefore, the goal in this paper is to perform a quantitative evaluation on Microsoft Azure, one of the IoT platform, about its advantages and disadvantages under press to determine if Azure IoT is fit for future IoT. In order to reach this goal, Azure IoT Hub is used as a bridge to connect and manage lots of IoT devices which send and receive huge amount of data. .NET is used to simulate devices and connect them to the IoT Hub. The two-way communication from sensor to cloud and from cloud to actuator is implemented through MQTT protocol. This paper makes measurements on three metrics including response time from sensor sending messages to actuator receiving messages, scalability and cost and analyzes them in detail. Besides, the analysis is also made in a specific scenario which has high demand on sensor update to see how Azure IoT performs. Finally, conclusion is made on Microsoft Azure IoT's advantages and disadvantages under stress.

This thesis describes how a solution can be built to detect human flu-like symptoms. Flu-like symptoms are important to detect to prevent Covid-19 [6]. As people are returning to work there is a need for a simple way of detecting flu-like symptoms to prevent the spread of Covid-19. Other than a solution, this thesis concluded how human flu-like symptoms can be detected, with cameras specifically. This is to know what symptoms are most likely to work for a prototype. The technique of cameras and thermal cameras made this project possible as well as the technique of a single-board computer. The technique of cloud-based services is also an important part of this project. This project has resulted in a novel prototype using a single-board computer, cameras, and various cloud-based services to detect and inform a person if he or she has a human flu-like symptom.

Denna avhandling beskriver hur en smartphone-baserad larm-applikation som ger säkrare resor för cyklister och löpare kan konstrueras. Genom att övervaka och utvärdera GPS-data från telefonen över tid skickar den föreslagna applikations-prototypen, namngiven till Are You OK App (AYOKA), automatiskt SMS-meddelanden och initierar telefonsamtal till kontakter i en lista konfigurerad av användaren när densamme har råkat ut för ett fall.I detta projekt härleds fall utifrån GPS-inaktivitet (när användarens geografiska koordinater är oförändrade inom ett valt tidsintervall). Detektion av GPS-inaktivitet, i kombination med att användaren inte har svarat, sätter igång larmfunktionen i applikationen. Projektet exemplifierar också hur implementeringen av ett flöde som delar data i ett moln, vilket använder Microsoft Azure som plattform, kan förbättra applikationens datainsamling avsevärt. Genom att använda en IoT Hub, Stream Analytics och en Azure SQL-databas, visar prototypen hur insamlad data kan centraliseras och potentiellt användas i framtida forskning inom övervakning och analys. Den testade prototypen visar ett förbättrat nöd- / säkerhetssystem som kan fungera i många olika sammanhang. Metoden för detektion passar relativt bra med fokus på cyklister och löpare eftersom dessa aktiviteter innebär att utövaren förflyttar sig, vilket i sin tur gör GPS-spårning effektiv. Några nackdelar som diskuteras är den höga grad av interaktion från användaren som behövs för att urskilja ett fall från en vald paus. För att möjliggöra detektering av fall från fysisk aktivitet som sker på en och samma geografiska plats, skulle det vara nödvändigt att i detekteringen använda data från accelerometer och gyroskop. I avhandlingen föreslås att prototypen, inklusive delnings-flödet för molndata, kan tjäna som ett ramverk för framtida system för smarta telefoner där fall-detektering använder sig av strömmad sensordata från enheten. / This thesis describes how a smartphone-based alarm application can be constructed to provide safer trips for cyclists and runners. Through monitoring and evaluating GPS data via the mobile device over time, the proposed application prototype, coined Are You OK App (AYOKA), automatically sends SMS messages and initiates phone calls to contacts in a user-configured list when a fall is detected. In this project, falls are inferred on the basis of GPS-inactivity (in this context defined as when the user’s geolocation has not changed within a selected time interval). Detection of GPS-inactivity, combined with a lack of response from the user, will trigger the alarming features of the application. The project also exemplifies how the implementation of a cloud data sharing flow, which uses Microsoft Azure as a platform, can significantly enhance the data gathering capabilities of the application. By utilizing an IoT Hub, Stream Analytics and an Azure SQL database, the prototype demonstrates how the gathered data can be centralized, and in future research could potentially be utilized for monitoring and analytical purposes. The method of detection performed relatively well with the focus on cyclists and runners since these activities involve changing of geographical coordinates, thereby making GPS-tracking effective. By focusing on detecting GPS-inactivity, it is argued that the prototype could potentially be utilized in other emergency scenarios apart from falls, such as being hit by a car. A disadvantage discussed includes the high degree of reliance on user participation to discern a fall from a voluntary pause. To enable detection of falls from physical activity occurring in one location, it would be necessary to incorporate data from accelerometer and gyroscope sensors into the current fall detection functionality. This thesis suggests that the prototype, including the cloud data sharing flow, can serve as a framework for future smartphone-based fall detection systems that use streamed sensor data.

The master thesis focuses on the design and implementation of data collection from production using a PLC into an SQL database located in the cloud and subsequent visualization. The work describes the applicable communication protocols MQTT and OPC UA with the fact that the protocol MQTT was selected. It deals with securing data transfer from the line to the cloud using the TLS protocol. The individual cloud services and their possibilities for data collection are described here. The work deals with the possibilities of data visualization using existing open source solutions and the differences between them. I describe the possibilities of modifying the open source environment of the Grafany project. Real dashboards from production are presented. The data collection system was deployed in two plants for testing.