This report consummates the Bachelor Project TI3806 course. To accumulate a Bachelor's degree in Computer Science and Engineering at the Delft University of Technology, it is compulsory to pass this course. The project, spanning a duration of ten weeks, is described in this report. The project client was Adviesgroep Strategisch Gebouwbeheer Nederland B.V. (ASG). This is a Dutch energy consultancy company based in Delft that uses big data innovations for energy management and for gaining insight into sustainability issues. The client encountered a real-life problem regarding the transmission of the data from their measuring devices. We were tasked to solve this problem. The project goal was to create a better data transmitter for the client. For naming convention purposes, we call the transmitter a gateway, as the device acts as a port between the measuring devices and the servers. It receives data from the measuring devices within its proximity and after filtering and processing this data, it is transmitted to the servers. The main objective was to design and develop a gateway that is better than the previously used gateway, the one that the client encountered numerous issues with. This improved gateway would then be deployed throughout the country such that the client enjoys numerous improvements. The most important improvements include cost reduction and a more robust, feature-rich and configurable solution. Furthermore, the goal of this report is to inform the reader about the work that has been completed during the duration of the project at ASG. This report also contains possible future recommendations for this project. The objective of the future recommendations is to act as advice, guidance and instructions for the continuity of this project as the client might decide to further expand the solution by co-workers.

Global warming causes coral bleaching which threatens the health and existence of coral reefs and therefore also the future of a lot of species, including human beings. Efforts to automate coral reef monitoring using annotated coral images to detect coral bleaching are hindered by the lack of a complete dataset that specifies the health and bleaching status of corals. We propose to combine publicly available data into a dataset and train a CNN for coral bleaching detection. This model performs surprisingly well. However, combining data from different sources gives rise to dataset biases which helps classifiers perform better and make them unreliable for unseen data. We try to detect such biases and document themsing several bias detection methods.