Dataset Collection of Multi-Communication Technologies Monitored in Different Mobility Contexts

The ubiquitous nature of mobile devices equipped with radio communication technologies made the collection of data a commonplace especially for studying human mobility. The collection of such datasets forms the intermediate results in many scientific research projects. Therefore, with the lack of datasets, collecting and publishing data should be seriously addressed since several scientific research is based on the gathering and analysis of measurement data. In this paper, we introduce the PILOT dataset, a Privacy-preserving data collectIon of wireLess cOmmunication Technologies. The dataset is a collection of four jointly collected information in different mobility contexts. It includes three wireless communication technologies: WiFi probe-responses, BLE (Bluetooth Low Energy) beacons, and LoRa (Long Range Radio) packets, plus additional information: Acceleration, Roll, and Pitch, all collected at the same time. We provide the keys to reproduce such data collection and share the datasets already collected. The dataset is collected for approximately 90 hours, with a size of 200 MB using FiPy devices from Pycom and it is uploaded to GitHub. The dataset’s utility is validated through the application of a classification machine learning model that determines the real-life situation of devices through the communication links monitored in different scenarios with an accuracy of $94 \%$. Thus, we believe that such dataset is important for human mobility studies and applications of integrated sensing systems since it offers a new form of a classified collected data that does not exist in the already published datasets.