Poster: LoRa Mobility and Coverage Dataset (LoRaMC)

Abstract

The rapid growth of the Internet of Things (IoT) and the emergence of Low Power Wide Area Network (LPWAN) technologies, such as LoRaWAN, have revolutionized how applications and services can leverage sensor and actuator devices. The truthful evaluation of applications and services relying on large-scale IoT deployments relies on the use of simulation tools in combination with datasets developed from real-world traces. In this work, we introduce a new dataset suitable for evaluating scenarios in urban environments that may involve mobile end-devices. The dataset includes an enriched taxi mobility data generated from 3300 mobile devices moving within a 200 square kilometer area that corresponds to the metropolitan city of Rome. The resulting dataset indicates for each device, it is position, which GWs received the frames, and with what radio characteristics. We believe that such a dataset can be used to highlight the effectiveness, scalability, and robustness of the LPWAN based system in the city-scale scenarios as well as help train and evaluate machine learning and artificial intelligence based methods that are embedded in network operation and resource optimization layers.