The Internet of Things (IoT) provides the ability to digitize physical objects into virtual data, thanks to the integration of hardware (e.g., sensors, actuators) and network communications for collecting and exchanging data. In this digitization process, however, security challenges need to be taken into account in order to prevent information availability, integrity, and confidentiality from being compromised. In this paper, security challenges of two broadly used technologies, RFID (Radio Frequency Identification) and Bluetooth, are analyzed. First, a review of the main vulnerabilities, security risk, and threats affecting both technologies are carried out. Then, open hardware and open source tools like: Proxmark3 and Ubertooth as well as BtleJuice and Bleah are used as part of the practical analysis. Lastly, risk mitigation and counter measures are proposed.

In this work, we describe a complete step-by-step workflow to apply machine-learning (ML) classification for chipless radio-frequency identification (RFID) tag identification, covering: 1) the tag implementation criteria for circular ring resonator (CRR) and square ring resonator (SRR) arrays for ML interoperability; 2) the data collection procedure to get a sufficiently representative dataset of real measurements; 3) the ML techniques to visualize the data and reduce its dimensionality; 4) the evaluation of the ML classifier to ensure high-accuracy predictions on new measurements; and 5) a thresholding scheme to increase the certainty of the predictions. The differences in the tags’ frequency responses are maximized by optimizing the Hamming distance between the tag identifiers (IDs) and by controlling each resonator array’s radar cross section (RCS) level. We show that the proposed workflow achieves perfect accuracy for the identification of four tags at a fixed distance of 160 cm. We also evaluate the performance of the proposed workflow to identify up to 16 tags within a flexible range (up to 140 cm), showcasing the tradeoff between the number of tags that can be correctly classified based on the reading range.

Chipless Radio Frequency Identification (RFID) offers a cost-effective and sustainable alternative to conventional RFID by eliminating integrated circuits in tags. In this work, we introduce a hybrid reader architecture for frequency-coded chipless RFID tags, combining the strengths of both time-domain and frequency-domain readers. The proposed system features a Gaussian impulse-based interrogation transmitter, realized using either a commercial impulse generator or a novel, low-cost logic gate-based impulse generator, and a Software-Defined Radio (SDR) for wideband spectral analysis during reception. This design is compact, self-contained, and low-cost, making it well-suited for large-scale deployments. Experimental evaluations demonstrate reliable resonance detection and tag identification, with results closely matching those from a Vector Network Analyzer (VNA). The reader achieves 1 MHz–6 GHz coverage and completes full-spectrum measurements in under 800 ms, showing less than 1.09% frequency deviation compared to the VNA. Furthermore, using the proposed reader, a machine learning-assisted tag identification experiment was conducted, yielding robust tag classification under noisy conditions, with accuracies of up to 98%.

CarTraC is a post-crash evidence-sharing proof-of-concept system for hit-and-run incidents, to cooperatively exchange crash data through direct V2V communication. The proposed architecture focuses on an overlooked aspect of V2V systems, post-crash evidence sharing, while prior research focuses on pre-crash safety. CarTraC detects collisions by monitoring motion data and upon detection automatically broadcasts encrypted and signed messages to nearby vehicles. These messages contain crash-related and vehicle-specific information retrievable by trusted authorities. The system operates without cellular networks or roadside infrastructure and supports two modes: Self-CarTraC, for direct data exchange, and Cooperative-CarTraC, where uninvolved vehicles assist in rebroadcasting. The security properties of the CarTraC protocol were formally verified using ProVerif. A proof-of-concept prototype was implemented on a custom ESP32-based embedded platform equipped with inertial sensors and a 2.4 GHz transceiver. Experimental validation was conducted using two stationary vehicles in an outdoor semi-urban environment, demonstrating reliable post-crash data exchange without external connectivity. The implementation achieved delivery rates of up to 100% at 15 m and 70% at 85 m, with end-to-end latencies below 34 ms.