BLE-WBAN: RF real-world dataset of BLE devices in human-centric healthcare environments
In communication and networking research, obtaining large, real-world datasets related to the physical layer has always been challenging, especially in IoT and Health IoT. In particular, there is a significant interest in datasets that help characterize physical layer properties without interferences from any other communications signals.
In this data repository, we introduce an open-source physical-layer dataset of Bluetooth Low Energy (BLE) IoT sensor devices recorded in an anechoic chamber using USRP x310. With a 100Msps sampling rate, it covers the entire BLE spectrum, featuring on-body and off-body scenarios with 13 BLE devices (ESP32s) from the same manufacturer. the goal is to study the physical layer characteristics of both on-body and off-body signals. The dataset is also available through MongoDB with a Python tool for analysis; for more details, please visit our GitHub page.
To access this database, the recommended method is through a MongoDB server, and you can find the guide on our GitHub page at GitHub Guide.
Additionally, the raw recordings are also accessible via IEEE Dataport for more in-depth exploration and detailed analysis.
The dataset is organized into two main folders, representing experiments conducted with two different Software Defined Radios (SDRs). Within these folders, you'll find experiments conducted both on and off the body for comprehensive research coverage. for more details please visit our GitHub repository
- SDR 2 files SDR_2.zip (14.23 GB)
- SDR 1 files SDR_1.zip (14.37 GB)
- Utillities to read the binary files and functions to do some plotting and processing on the data dataProcessing.py (17.99 kB)