Real-time detection of X/gamma radiation dose rates holds particular significance in nuclear science research. In this study, we developed a portable X/gamma-ray survey meter for large-scale distributed real-time monitoring of ambient dose equivalent rates in the surrounding environment. This innovative device uses a silicon photomultiplier coupled with a CsI(Tl) scintillator and can connect to an Internet of Things (IoT) network.


The human gait is unique and so is the impact of a walking human on the propagation of wireless signals within a wireless network. Using appropriate pattern recognition techniques, a person can thus be identified just from a time series of Received Signal Strength (RSS) measurements. This dataset holds bidirectional RSS measurements recorded within a mesh network of four Bluetooth sensor devices. During the measurements, a total of 14 subjects walked individually through the setup. A total of more than 10,000 recordings are provided.


This article presents the details of the Cardinal RF (CardRF) dataset. CardRF is acquired to foster research in RF- based UAV detection and identification or RF fingerprinting. RF signals were collected from UAV controllers, UAV, Bluetooth, and Wi-Fi devices. Signals are collected at both visual line-of-sight and beyond-line-of-sight. The assumptions and procedure for the data acquisition are presented. A detailed explanation of how the data can be utilized is discussed. CardRF is over 65 GB in storage memory.


The number of private vehicles is still increasing from year to year. In order to limit environmental damage, a proper way of dealing with this trend is the introduction of intelligent automotive infrastructure. Besides traffic management solutions, smart parking guidance systems are important for reducing unnecessary traffic. For this, a key prerequisite are sensor networks that provide information about the occupancy state of every single parking spot in the parking infrastructure of high traffic targets e.g. nearby an airport or shopping mall.


The Bluetooth 5.1 Core Specification brought Angle of Arrival (AoA) based Indoor Localization to the Bluetooth Standard. This dataset is the result of one of the first comprehensive studies of static Bluetooth AoA-based Indoor Localization in a real-world testbed using commercial off-the-shelf Bluetooth chipsets.

The positioning experiments were carried out on a 100 m² test area using four stationary Bluetooth sensor devices each equipped with eight antennas. With this setup, a median localization accuracy of up to 18 cm was achieved.