GammaSense: Infrastructureless Positioning Using Background Radioactivity

We introduce the harvesting of natural background radioactivity for positioning. Using a standard Geiger-Muller counter as sensor, we fingerprint the natural levels of gamma radiation with the aim of then roughly pinpointing the position of a client in terms of interfloor, intrafloor, and indoor-versus-outdoor locations. We find that the performance of a machine-learning algorithm in detecting position varies with the building, and is highest for interfloor detection in the case of an old domestic house, while it is highest for intrafloor detection if the floor spans building segments made from different construction materials. Altogether, the technique has lower performance than infrastructure-based localization techniques.

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