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This thesis deals with notification between smart phone and application server which controls intelligent home. It is focused on Android mobile platform and it is implemented for that platform. Notification service Google Cloud Messaging is used because it is not possible to directly address mobile phones from server. Based on actual position and user defined area the server is informed about moving to or from the area. The server can perform defined event automatically with these information. Implementation was tested in real system. Alpha and beta versions were regularly published.

Background: As cloud computing has risen over the last decades, there are several cloud services accessible on the market, users may prefer to select those that are more flexible and efficient. Based on the preceding, we chose to research to evaluate cloud services in terms of which would be better for the user in terms ofgetting the needed data from the chosen website and utilizing JMeter for performance testing. If we continue our thesis study by assessing the performance of different sample users using JMeter as the testing tool, it is appropriate for our thesis research subject. In this case, the user interfaces of GCP and AWS are compared while doing several compute engine-related operations. Objectives: This thesis aims to test the website performance after deploying in two distinct cloud platforms.After the creation of instances in AWS, a domain in GCP and also the bucket, the website files are uploaded into the bucket. The GCP and AWS instances are connected to the lifestyle store website. The performance testing on the selected website is done on both services, and then comparison ofthe outcomes of our thesis research using the testing tool Jmeter is done. Methods: In these, we choose experimentation as our research methodology,and in this, the task is done in two cloud platforms in which the website will be deployed separately. The testing tool with performance testing is employed. JMeter is used to test a website’s performance in both possible services and then to gather our research results, and the visualization of the results are done in an aggregate graph, graphs and summary reports. The metrics are Throughput, average response time, median, percentiles and standard deviation. Results: The results are based on JMeter performance testing of a selected web-site between two cloud platforms. The results of AWS and GCP can be shown in the aggregate graph. The graph results are based on the testing tool to determine which service is best for users to obtain a response from the website for requested data in the shortest amount of time. We have considered 500 and 1000 users, and based on the results, we have compared the metrics throughput, average response time, standard deviation and percentiles. The 1000 user results are compared to know which cloud platform performs better. Conclusions: According to the results from the 1000 users, it can be concluded that AWS has a higher throughput than GCP and a less average response time.Thus, it can be said that AWS outperforms GCP in terms of performance.

The usage of cloud computing in order to move away from local servers and infrastructure have grown enormously the last decade. The ability to quickly scale capacity of servers and their resources at once when needed is something that can both be a price saver for companies and help them deliver high end products that will function correctly at all times even under heavy load to their customers. To meet todays challenges, one of the strategic directions of Attentec, a software company located in Linköping, is to examine the world of cloud computing in order to deliver robust and scalable applications to their customers. This thesis investigates the usage of cloud services in order to deploy scalable applications which can adapt to usage peaks within minutes.

The diploma thesis deals with automated measurement of humidity and CO2 concentration inside buildings. Results of this measurement form the input parameters for the effective management of technologies reducing the energy performance of buildings. In the introduction, the issue of indoor air quality of buildings and indicators characterizing this quality are approached. The technical part of the thesis consists of making a measuring device which contains two prototype sensors provided by Teco Inc. with online access to measured data. The measurement of relative humidity, CO2 concentration and temperature in the interior of the building with the made device is compared with commercially available devices for measuring selected parameters. For unambiguous interpretation of online data, the virtual machine with an online database is configured for the created measuring device. The possibilities of using the prepared measuring device to achieve a reduction in the energy performance of buildings are discussed in the final part.

The Domain Name System (DNS) oversees the internet's architecture, providing pointers to both internal and external services. Consequently, enterprises increase their attack surface while simultaneously increasing their exposure to potential cyber threats. Subdomain takeovers happen when a subdomain leads to a website that no longer exists. As a result, the subdomain will be in control of an attacker. A compromised subdomain may be the access point to many attacks like information threats, phishing attacks, infrastructure intrusion and many more. Subdomain takeover attacks are one of the overlooked attack surfaces related to cyber security. This thesis aims to investigate the subdomain takeover attacks, how the attacks happen, the attack methodology by an attacker and drawbacks in the current strategies and tools, which are countermeasures for subdomain takeover attacks. The research focuses on resolving an intrusion from happening within the perspective of an enterprise standpoint. A new custom framework which resolves the subdomain takeover attacks was developed. A comparative study of the newly developed framework and the existing open-source tools and their response to an attack scenario too is made. Also, a comparison of the leading cloud platforms was conducted and their existing security features and mitigation measures for similar attacks and threats.

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Introduction: Instead of replacing existing analogue water meters with Internet of Things (IoT) connected substitutes, an alternative would be to attach an IoT connected module to the analogue water meter that optically reads the meter value using Optical Character Recognition (OCR). Such a module would need to be battery-powered given that access to the electrical grid is typically limited near water meters. Research has shown that offloading the OCR process can reduce the power dissipation from the battery, and that this dissipation can be reduced even further by reducing the amount of data that is transmitted.  Purpose: For the sake of minimising energy consumption in the proposed solution, the purpose of the study is to find out to what extent it is possible to reduce an input image’s file size by means of resolution, colour depth, and compression before the Google Cloud Vision OCR engine no longer returns feasible results.   Method and implementation: 250 images of analogue water meter values were processed by the Google Vision Cloud OCR through 38 000 different combinations of resolution, colour depth, and upscaling.  Results: The highest rate of successful OCR readings with a minimal file size were found among images within a range of resolutions between 133 x 22 to 163 x 27 pixels and colour depths between 1- and 2-bits/pixel.  Conclusion: The study shows that there is a potential for minimising data sizes, and thereby energy consumption, by offloading the OCR process by means of transmitting images of minimal file size.

In the data-driven landscape of modern business, predictive analytics plays a pivotal role inanticipating and mitigating customer churn—a critical challenge for organizations. However, thetraditional complexities of machine learning hinder accessibility for decision-makers. EnterMachine Learning as a Service (MLaaS), offering a gateway to predictive modeling without theneed for extensive coding or infrastructure.This thesis presents a comprehensive evaluation of cloud-based and cloud-agonostic AutoML(Automated Machine Learning) platforms for customer churn prediction. The study focuses onfour prominent platforms: Azure ML, AWS SageMaker, GCP Vertex AI, and Databricks. Theevaluation encompasses various performance metrics including accuracy, AUC-ROC, precision,recall to assess the predictive capabilities of each platform. Furthermore, the ease of use andlearning curve for model development are compared, considering factors such as data preparation,training steps, and coding requirements. Additionally, model training times are analyzed toidentify platform efficiencies. Preliminary results indicate that AWS SageMaker exhibits thehighest accuracy, suggesting strong predictive capabilities. GCP Vertex AI excels in AUC,indicating robust discriminatory power. Azure ML demonstrates a balanced performance,achieving notable accuracy and AUC scores. Databricks being platform independent is a winnerand has also shown good metrics. Its capability to generate notebook is an added advantage whichcan be modified by experts to fine tune the results more. This research provides valuable insightsfor organizations seeking to implement different AutoML solutions for customer churnprediction.

This project is about trying to apply machine learning theories on a selection of data points in order to see if an improvement of current methodology within stress detection and measure selecting could be applicable for the company Linkura AB. Linkura AB is a medical technology company based in Linköping and handles among other things stress measuring for different companies employees, as well as health coaching for selecting measures. In this report we experiment with different methods and algorithms under the collective name of Unsupervised Learning, to identify visible patterns and behaviour of data points and further on we analyze it with the quantity of data received. The methods that have been practiced on during the project are “K-means algorithm” and a dynamic hierarchical clustering algorithm. The correlation between the different data points parameters is analyzed to optimize the resource consumption, also experiments with different number of parameters are tested and discussed with an expert in stress coaching. The results stated that both algorithms can create clusters for the risk groups, however, the dynamic clustering method clearly demonstrate the optimal number of clusters that should be used. Having consulted with mentors and health coaches regarding the analysis of the produced clusters, a conclusion that the dynamic hierarchical cluster algorithm gives more accurate clusters to represent risk groups were done. The conclusion of this project is that the machine learning algorithms that have been used, can categorize data points with stress behavioral correlations, which is usable in measure testimonials. Further research should be done with a greater set of data for a more optimal result, where this project can form the basis for the implementations. / Detta projekt handlar om att försöka applicera maskininlärningsmodeller på ett urval av datapunkter för att ta reda på huruvida en förbättring av nuvarande praxis inom stressdetektering och  åtgärdshantering kan vara applicerbart för företaget Linkura AB. Linkura AB är ett medicintekniskt företag baserat i Linköping och hanterar bland annat stressmätning hos andra företags anställda, samt hälso-coachning för att ta fram åtgärdspunkter för förbättring. I denna rapport experimenterar vi med olika metoder under samlingsnamnet oövervakad maskininlärning för att identifiera synbara mönster och beteenden inom datapunkter, och vidare analyseras detta i förhållande till den mängden data vi fått tillgodosett. De modeller som har använts under projektets gång har varit “K-Means algoritm” samt en dynamisk hierarkisk klustermodell. Korrelationen mellan olika datapunktsparametrar analyseras för att optimera resurshantering, samt experimentering med olika antal parametrar inkluderade i datan testas och diskuteras med expertis inom hälso-coachning. Resultaten påvisade att båda algoritmerna kan generera kluster för riskgrupper, men där den dynamiska modellen tydligt påvisar antalet kluster som ska användas för optimalt resultat. Efter konsultering med mentorer samt expertis inom hälso-coachning så drogs en slutsats om att den dynamiska modellen levererar tydligare riskkluster för att representera riskgrupper för stress. Slutsatsen för projektet blev att maskininlärningsmodeller kan kategorisera datapunkter med stressrelaterade korrelationer, vilket är användbart för åtgärdsbestämmelser. Framtida arbeten bör göras med ett större mängd data för mer optimerade resultat, där detta projekt kan ses som en grund för dessa implementeringar.