Wearable devices, such as headsets and activity trackers, rely heavily on the Bluetooth and/or the Bluetooth Low Energy wireless communication standard to exchange data with smartphones or other peripherals. Since these devices collect personal health and activity data, ensuring the privacy and security of the transmitted data is crucial. Therefore, we present a dataset that captures complete Bluetooth communications—including advertising, connection, data exchange, and disconnection—in an RF isolated environment using software-defined radio. We were able to successfully decode the captured Bluetooth packets using existing tools. This dataset provides researchers with the ability to fully analyze Bluetooth traffic and gain insight into communication patterns and potential security vulnerabilities.

This study investigates the integration of audio features with CO₂ sensor data to enhance occupancy detection accuracy in naturally ventilated office environments. Accurate occupancy detection is pivotal for smart building energy management, yet CO₂-based methods cannot provide fast enough response times and are sensitive to air circulation changes due to internal convection. In this article we propose a combination of CO₂ sensors and audio features from MEMS microphones to improve the occupancy detection accuracy and improve the response times. We use a Random Forest classifier and evaluate the results across two scenarios: CO₂-only and CO₂ combined with audio features. Results show that incorporating the audio features into the occupancy detection algorithms yields a significant increase in detection accuracy and speed, especially when the environment is subject to frequent air circulation changes due to internal convection, like the opening and closing of windows and doors. Combining the CO₂ and audio sensing offers a promising, cost-effective approach to occupancy detection in smart buildings, yet more research on advanced audio processing and feature selection is necessary.

This paper presents a thermal and power characterization of the NVIDIA Jetson AGX Orin deployed in a realistic outdoor edge AI enclosure, integrated with power supplies, a Long Term Evolution (LTE) module, and a thermostat-controlled fan, across an ambient temperature range from −20 °C to +40 °C. The device was tested in a climate chamber under two workloads: a synthetic CPU and GPU stress test, and a YOLOv8s inference workload (TensorRT FP16, 640 × 640 input). Internal temperatures were recorded using four calibrated platinum Resistance Temperature Detectors (RTD)—PT100/PT1000, while Jetson chip temperatures and power consumption were logged via tegrastats and jtop. At sub-zero ambient temperatures, heat dissipated by the device itself kept all components within their operating ranges down to −20 °C. At the +40 °C setpoint, the stress test triggered Jetson thermal throttling at GPU and CPU temperatures of +95.6 °C and +99.0 °C, respectively. Under the same conditions, the YOLOv8s inference workload sustained 108.8 frames per second (FPS) at 19.1 W average power, approximately half of the 36.5 W consumed under the stress test, with chip temperatures well below the throttling threshold. These findings indicate that synthetic stress tests substantially overestimate the thermal and power demands of the tested inference workload, and that the enclosure retains sufficient thermal headroom for outdoor edge device deployment.

This paper presents an experimental study on radio frequency (RF) fingerprinting of Bluetooth Classic devices. Our research aims to provide a practical evaluation of the possibilities for RF fingerprinting of everyday Bluetooth connected devices that may cause privacy risks. We have built an experimental setup for recording Bluetooth connection in a radio frequency isolated environment using commercially available SDR (software defined radio) systems, extracted fingerprints of the Bluetooth radio data in the form of carrier frequency offset and scaling factor from 6 different devices, and performed k-nearest neighbors (kNN) classification achieving 84\% accuracy. The experiment demonstrates that no matter what privacy measures are being taken in the protocol layer, the physical layer leaks significant information about the device to unauthorized listeners. In the context of the ever-growing Bluetooth device market, this research serves as a clarion call for device manufacturers, regulators, and end-users to acknowledge the privacy risks posed by RF fingerprinting and lays a foundation for more sizeable Bluetooth fingerprinting analysis research.