Radio Frequency Identification (RFID) tags have been used to identify tagged objects when they are interrogated by expensive devices known as RFID readers. An RFID reader sends an RF signal toward a tag, and by reflecting that signal the tag transmits information. In this project, the starting assumption is that RFID tags are enhanced and read the reflected RF signal from another transmitting tag. The signal reflected by the transmitting tag is either an ambient RF signal or signal generated by a dedicated inexpensive RF source. This eliminates the expensive RFID readers from the networks. Intelligence and collaboration are added to a network of such tags based on a novel method for estimation of tag-to-tag communication channels. This enables the network to localize and recognize dynamic events that take place in the proximity of the tags. The new capabilities of the tags to gather unique environmental data will unlock a large number of applications in the Internet-of-Things domain which in turn will bring benefits to society in various forms including improved safety, comfort, efficiency and better decision making. The team of researchers will contribute to enhancing the offerings of after-school activities for female high school students via Stony Brook's Women in Engineering and Science Honors Program. Further, the team will be engaged with the daily activities of high-school students through the engineering teaching laboratories in the Electrical and Computer Engineering department.
The goal and scope of the proposed work include the development of autonomous networks of battery-less, RF-powered tags, which besides performing basic operations such as localization and tracking, carry out RF sensing and fingerprinting. This is accomplished by novel techniques for channel measurement based only on passive backscattering and by processing these measurements to recognize surrounding activities. The research on this project brings the following contributions: 1) novel battery-less RF tag architectures that operate with a few microwatts of available power and that are capable of passive channel estimation and necessary communication and processing abilities; 2) effective distributed algorithms for tag selection for channel measurements, information fusion and learning techniques to obtain the best possible estimates given various constraints; 3) novel collaborative backscattering techniques that exploit channel estimation; 4) building discrete and integrated circuit versions of the RF tags which will be used for creating a tag network in the lab and in more realistic smart home environments for evaluating the proposed techniques.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
