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A Radio Environment Map (REM) is a powerful tool in enhancing the experienceof radio-enabled agents but building such a REM can be a laborious undertaking,especially in three dimensions. This project shows how such a REM of an indoorthree-dimensional space can be generated in an autonomous and scalable way.Building on the results of the preceding Research Project 1, multiple dronesare used to map the WiFi signals present in such a space in a real-worldenvironment where the drones are each able to visit 36 waypoints andcollectively gather thousands of WiFi beacon data samples. This report alsoincludes an analysis of the collected data and concludes by proposingmachine-learning based techniques to predict the signal strength of knownaccess points in locations not visited by the drones.

Research Project 2: Drone-supported AI-based Generation of 3D Maps of Indoor Radio Environments