Syftet med studien var att få en inblick i informationssäkerhet hos organisationer inom Gävleborg och kunna jämföra hur privata och offentliga sektorn arbetar. En kvalitativ studie utfördes för att samla in data från organisationer i Gävleborg. Fem respondenter deltog i semistrukturerade intervjuer. Intervjuerna var på distans och genomfördes med hjälp av videokonferens-tjänster via nätet. Alla organisationer i studien följer ett antal lagar, regler och direktiv vid hantering av information. Det finns en skillnad i vad den privata sektorn behöver följa gentemot den offentliga sektorn. För att upprätthålla informationssäkerheten deltar alla medarbetare inom organisationerna i någon form av utbildning. Informationsklassificeringen sker på olika sätt inom organisationerna

Syftet med det här arbetet är att undersöka hur överföringstid skiljer sig i förhållande tillskalbarhet mellan två back-endlösningar i LoRaWAN. Lösningarna är Node-RED ochChirpstack. Studien genomförs med hjälp av metoderna experiment och fallstudie. Iexperimentet utfördes ett t-test för att svara på om nollhypotesen kan förkastas eller inte.Resultatet visar att nollhypotesen inte kunde förkastas, och att det inte är någon skillnadi skalbarhet. Bortsett från t-testet visar resultaten att Chirpstack skalar sämre änNode-RED vid fyra eller fler tillagda enheter. Slutsatsen är däremot att skillnaden ärtillräckligt liten för att bedömas obetydlig för val av back-endlösning. Studien ärbegränsad till sju uppkopplade LoRa-enheter. / The purpose of this study is to investigate how transfer time differs in relation toscalability between two back-end solutions in LoRaWAN. The solutions are Node-REDand Chirpstack. The study is carried out using the methods experiment and case study.In the experiment, a t-test was performed to answer whether the null hypothesis can berejected or not. The results show that the null hypothesis could not be rejected, and thatthere is no difference in scalability. Apart from the t-test, the results show that Chirpstackscales worse than Node-RED at four or more added LoRa-units. The conclusion,however, is that the difference is small enough to be considered insignificant for thechoice of back-end solution. The study is limited to seven connected LoRa units.

This master's thesis evaluates data annotation, semantic segmentation and Docker for use in AWS. The data provided has to be annotated and is to be used as a dataset for the creation of a neural network. Different neural network models are then to be compared based on performance. AWS has the option to use Docker containers and thus that option is to be examined, and lastly the different tools available in AWS SageMaker will be analyzed for bringing a neural network to the cloud. Images were annotated in Ilastik and the dataset size is 276 images, then a neural network was created in PyTorch by using the library Segmentation Models PyTorch which gave the option of trying different models. This neural network was created in a notebook in Google Colab for a quick setup and easy testing. The dataset was then uploaded to AWS S3 and the notebook was brought from Colab to an AWS instance where the dataset then could be loaded from S3. A Docker container was created and packaged with the necessary packages and libraries as well as the training and inference code, to then be pushed to the ECR (Elastic Container Registry). This container could then be used to perform training jobs in SageMaker which resulted in a trained model stored in S3, and the hyperparameter tuning tool was also examined to get a better performing model. The two different deployment methods in SageMaker was then investigated to understand the entire machine learning solution. The images annotated in Ilastik were deemed sufficient as the neural network results were satisfactory. The neural network created was able to use all of the models accessible from Segmentation Models PyTorch which enabled a lot of options. By using a Docker container all of the tools available in SageMaker could be used with the created neural network packaged in the container and pushed to the ECR. Training jobs were run in SageMaker by using the container to get a trained model which could be saved to AWS S3. Hyperparameter tuning was used and got better results than the manually tested parameters which resulted in the best neural network produced. The model that was deemed the best was Unet++ in combination with the Dpn98 encoder. The two different deployment methods in SageMaker was explored and is believed to be beneficial in different ways and thus has to be reconsidered for each project. By analysis the cloud solution was deemed to be the better alternative compared to an in-house solution, in all three aspects measured, which was price, performance and scalability.

Elasticsearch has evolved from an experimental, open-source, NoSQL database for full-text documents to an easily scalable search engine that canhandle a large amount of documents. This evolution has enabled companies todeploy Elasticsearch as an internal search engine for information retrieval (logs,documents, etc.). Later on, it was transformed as a cloud service and the latestdevelopment allows a containerized, serverless deployment of the application,using Docker and Kubernetes.This research examines the behaviour of the system by comparing the length and appearance of single-term and multiple-terms queries, the scaling behaviour and the security of the service. The application is deployed on Google Cloud Platform as a Kubernetes cluster hosting containerized Elasticsearch images that work as databasenodes of a bigger database cluster. As input data, a collection of JSON formatted documents containing the title and abstract of published papersin the field of computer science was used inside a single index. All the plots were extracted using Kibana visualization software. The results showed that multiple-term queries put a bigger stress on thesystem than single-term queries. Also the number of simultaneous users querying in the system is a big factor affecting the behaviour of the system. By scaling up the number of Elasticsearch nodes inside the cluster, indicated that more simultaneous requests could be served by the system.

Blockchain technology has become an appealing concept in Distributed Systems because it enables a distributed storage of information, replacing a central database [1]. In addition, Blockchains promise to address inherent and difficult issues in distributed systems such as a) proving the provenance of information, i.e., the documentation where pieces of data comes from (including their the processing), and b) that the information has not been changed, i.e., the integrity of the information has not been corrupted. The data in a Blockchain is said to be immutable. In this thesis, we apply Blockchain technology as a concept in Distributed Systems for securely collecting and storing data from distributed cloud resources that must be intact over a longer amount of time, such as the amount of consumed cloud resources characterized by CPU load or energy usage. In particular, this work considers a peer-to-peer energy use case where virtual energy resources are monitored. The focus of this thesis is on a) how a Blockchain for a distributed Cloud monitoring can be implemented, b) how the workload can be characterized and c) how the Blockchain system’s performance can be observed and what performance can be achieved. Therefore, the work defines an initial system model, provide an implementation, and carries out experiments in order to understand the impact of the design factors and the system input to the capabilities and performance of the system. The results of the experiments, the workload characterization and performance analysis, are analysed by statistical means and provided as graphs. The choices of system models, Blockchain technology (Hyperledger Fabric), and other parameters, are based on the literature review. The experimental implementation is, in turn, based on the selected system model, where we want to experimentally identify limitations and bottlenecks of the performance.

This thesis describes how to build a solution for transmitting data from an           Electroencephalography (EEG) device to a server in real-time while guiding the user through a number of predefined exercises. This solution will be used by Spinal Cord Injury (SCI) patients suffering from neuropathic pain, in order to understand if it is possible to predict such pain from EEG. The collected data will help clinicians analyze the brain activity data from patients who can submit the data from their home. To accomplish this development task, an application was built that connects to a portable EEG device, gather brain activity data from patients, guides patients through a set of imaginary tasks and sends the data to a server. This project made use of a Software Development Kit (SDK) for the Python programming language and a web sockets server written in JavaScript. The application was tested both in terms of usability and end-to-end latency, showing high usability and low latency. The proposed solution will support a clinical trial in Spain with 40 SCI patients.

Hydropower dams have been helping our society with electricity production for a long time now. In the past there have been several serious dam accidents in many parts of the world and the continuous work with dam safety is very important to Swedish hydropower industry. There are many measurements that must be recorded and analysed in order to guarantee safe operations and there are several ongoing digitisation projects that focus on that area of interest. This master thesis project was done in cooperation with Vattenfall R&D and its main focus was measuring of the water level in open stand pipes with wireless sensors using LoRa radio technology. This aspect is important since this water level represents the uplift pressure that acts on gravity dams and how it varies over time. The goal of this thesis was to accumulate knowledge about wireless transmission from sensors and to analyse and evaluate the proposed, LoRa-based system. This goal was met by conducting a literature study about wireless transmission from sensors and different aspects of the LoRa-technology, performing practical test of the proposed system and finally by doing a risk analysis with focus on potential attack paths into the system. The study showed that even though LoRa is a quite new technology, it has matured fast enough to be of interest for dam safety monitoring applications and it also provides relatively high level of security in the most cases. There are unfortunately several methods of jamming or disrupting the system, which could be potentially used by a malicious entity. The motivation level for this kind of attack was deemed to be low since the data transferred is of no interest for a third party. Apart from such potential attacks, LoRa is a technology that is well suited for applications in the hydroelectric sector that do not require large transmission capacities because of its low energy usage and the ability to connect to several sensors placed at larger distances. Several minor problems were discovered during the practical tests that were conducted during this project but those were solved quickly in dialogue with the supplier, Iot AB and the manufacturer, Keller. The nature of those tests was quite improvised since a standardised test procedure for such innovative solutions is lacking. Nevertheless, those tests were concluded with satisfying results. The test installation of the system at the power plant was performed well and all parts of the system functioned as expected. The results pointed to the conclusion that the system was functioning correctly and there is a good potential for a wide implementation despite its potential security risks. Teknisk-naturvetenskapliga fakulteten, Uppsala universitet. Utgivningsort U ppsal a/Visby. H andledare: Christi an Bernstone, Äm nesgranskare: Urban Lundin, Exami nator: Petra Jönsson

The procedure of testing the implemented software is important and should be an essential and integrated part of the development process. In order for the testing to be meaningful it is important that the testing procedure ensures that the developed software meet certain requirements. The testing procure is often controlled by some sort of test specification. For many companies it is desirable to automate this procure. The focus of this thesis has been to automate a small subpart of the manual tests today performed related to SAAB:s air traffic management system. The automation has been achieved by studying the existing test specification which involves a lot of manual operations and to write software that mimics a few of these test cases. The thesis has resulted in a test framework which automates a small subset of the manual tests performed today. The framework has been designed to be scalable and to easily allow more test cases to be added by the personnel when time permits. The test framework has also been integrated with SAAB:s existing CI/CD workflow.

Research conducted in the aviation industry includes two major areas, increased safety and a reduction of the environmental footprint. This thesis investigates the possibilities of increased situational awareness with computer vision in avionics systems. Image fusion methods are evaluated with appropriate pre-processing of three image sensors, one in the visual spectrum and two in the infra-red spectrum. The sensor setup is chosen to cope with the different weather and operational conditions of an aircraft, with a focus on the final approach and landing phases. Extensive image quality assessment metrics derived from a systematic review is applied to provide a precise evaluation of the image quality of the fusion methods. A total of four image fusion methods are evaluated, where two are convolutional network-based, using the networks for feature extraction in the detailed layers. Other approaches with visual saliency maps and sparse representation are also evaluated. With methods implemented in MATLAB, results show that a conventional method implementing a rolling guidance filter for layer separation and visual saliency map provides the best results. The results are further confirmed with a subjective ranking test, where the image quality of the fusion methods is evaluated further.

The rise of IoT-based system is making an impact on our daily lives and environment. Fog Computing is a paradigm to utilize IoT data and process them at the first hop of access network instead of distant clouds, and it is going to bring promising applications for us. A mature framework for fog computing still lacks until today. In this study, we propose an approach for monitoring fog nodes in a distributed system using the FogFlow framework. We extend the functionality of FogFlow by adding the monitoring capability of Docker containers using cAdvisor. We use Prometheus for collecting distributed data and aggregate them. The monitoring data of the entire distributed system of fog nodes is accessed via an API from Prometheus. Furthermore, the monitoring data is used to perform the ranking of fog nodes to choose the place to place the serverless functions (Fog Function). The ranking mechanism uses Analytical Hierarchy Processes (AHP) to place the fog function according to resource utilization and saturation of fog nodes’ hardware. Finally, an experiment test-bed is set up with an image-processing application to detect faces. The effect of our ranking approach on the Quality of Service is measured and compared to the current FogFlow.