Battery-Less RFID Systems for IoT Applications

The following PhD thesis deals about the implementation and testing of smart batteryless Radio Frequency IDentification (RFID)-based systems for Internet of Things (IoT) applications. In particular, the research focuses on the main aspects of RFID technology, such as localization, sensing, and robot collision avoidance (based on self-tuning tags). All applications are characterized by the features of low-cost, battery-less, and automatic data collection, suitable for IoT and Industry 4.0 context. First, the thesis introduces a generic RFID system together with a state-of-the-art regarding its application in retail. The literature shows that it is possible to reach 99% of tag readings under certain conditions.1 Integration of a robot with the RFID transmission side (reader and antenna) leads directly to the first feature, which is indoor localization. The method is based on Synthetic Aperture Radar (SAR) principle. The robot carrying one or more antennas can collect phase data from passive RFID tags to evaluate the spatial position of a target tag. It is possible to use one or more antennas with different configurations (static or rotating). Experimental results demonstrated that using two rotating antennas presents the lowest localization error in the majority of cases. RFID tags can be integrated with a sensor interface, which is the main argument in the third chapter. The section foresees a study on the main RFID sensor tags present on the market, together with the development of Battery Assisted Passive (BAP) RFID sensor tag. The sensor tag was designed starting from a Ultra High Frequency (UHF) antenna simulated on CST Studio Suite and concludes with the integration of electronic components, a sensor board, and a moisture sensor. In general, results showed that for acquiring sensing data from passive tags, a stronger power level and/or a shorter antenna-tag distance are necessary with respect to the localization feature. Finally, a collision avoidance system based on self-tuning RFID tags was implemented for safety applications in low-speed robotic environments. The robot can detect obstacles through the reading of the sensor code received from a self-tuning tag and comparing that value with a distance threshold. Danger and warning zones are defined to classify the importance of an obstacle. The thesis concludes by presenting some complementary activities. The first was about an activity that regards the energy harvested from an RF source at 868 MHz. That frequency falls in the European Telecommunications Standards Institute (ETSI)-RFID bands. Another activity regards my period abroad, where an RFID array for anti-theft applications was studied and tested. Finally, the last activities reported are related to my collaboration with Partitalia Srl. In particular, an automatic testing system for RFID tags and a RFID gate for retail application that foresees a forklift passage were tested.