An IoT-aware Architecture to improve Safety in Sports Environments

The introduction of Internet of Things enabling technologies into the sport and recreational activities domain provide an interesting research challenge. Their adoption could significantly improve the sport experience and also the safety level of team sports. Despite this, only few attempts have been done to demonstrate the benefits provided by use of IoT technologies in sport environments. To fill this gap, this paper propose an IoT-aware Sport System based on the jointly use of different innovative technologies and standards. By exploiting the potentialities offered by an ultra-low-power Hybrid Sensing Network (HSN), composed of 6LoWPAN nodes integrating UHF RFID functionalities, the system is able to collect, in real time, both environmental parameters and players’ physiological data. Sensed data are then delivered to a Cloud platform where a monitoring application makes them easily accessible via REST Web Services. A simple proof of concept has demonstrated the appropriateness of the proposed solution

Electromagnetic Performance Estimation of UHF RFID Tags in Harsh Contexts

Radio-Frequency Identification (RFID) technology is a consolidated example of electromagnetic system in which passive labels equipped with flexible antennas, called tags, are able to use a portion of the electromagnetic energy from the reader antennas, power-up their internal circuitry and provide the automatic identification of objects. Being fully-passive, the performance of RFID tags is strongly dependent on the context, so that the selection of the most suitable tag for the specific application becomes a key point. In this work, a cost-effective but accurate system for the over-the-air electromagnetic characterization of assembled UHF RFID tags is firstly presented and then validated through comparison with a consolidated and diffused measurement systems. Moreover, challenging use-cases demonstrating the usefulness of the proposed systems in analyzing the electromagnetic performance of label-type tags also when applied on materials on different shape or embedded into concrete blocks have been carried out

Using Battery-Less RFID Tags with Augmented Capabilities in the Internet of Things

Driven by user demand for new smart systems in the framework of the Internet of Things (IoT) and fueled by technological advances in Radiofrequency Identification (RFID), an increasing number of new IoT-oriented RFID-based devices has appeared in recent years in scientific literature. Some of them conjugate canonical RFID identification with extra functionalities such as sensing, reasoning, memorization, and actuation. In this way, IoT challenging applications can be developed, which distribute processing load till to the extreme nodes of the network, while lying upon the well-established RFID infrastructure. In this work, a reasoned panoramic on the potentialities in the IoT framework of augmented RFID tags is presented and classified. Two applicative scenarios are envisioned, presented and discussed, to illustrate how augmented RFID devices may support advanced IoT systems

Differential RCS and Sensitivity Calculation of RFID Tags with Software-Defined Radio

Performance evaluation of passive Radio Frequency IDentification (RFID) tags is a challenging task. In fact, tag performance depends on multiple factors such as the goodness of the conjugate matching between chip and antenna, the chip sensitivity, the strength and quality of the backscattered signal. Commercially available solutions for tag testing are very expensive and not totally flexible. In this work, we propose a novel approach for precise characterization of RFID tags based on Software-Defined Radio (SDR). We show how a cheap (below 1000$) and flexible SDR-based RFID reader can be turned into an accurate tool for measuring the tag sensitivity and differential radar cross-section. We test our platform by analyzing the performance of two built-in-lab tags: measurements show a strong agreement with theoretical and simulation results