NeTS: Small: Collaborative Research: Low-cost, Convenient, Non-intrusive Methods for Enabling the Internet of Things

University Of California-Santa Cruz, Santa Cruz CA

Investigators

Abstract

The Internet of things (IoT) has been gaining momentum in both industry and academic communities. From phones to smart phones, from electricity meters to smart meters, from cars to connected vehicles, new products have been developed with enhanced capabilities in data communications, sensing, and information exchange, thanks to rapid advance in embedded systems and sensor networks, which integrate miniaturized computing devices with the target objects. However, this approach of replacing old products with new designs can be expensive and sometimes inconvenient. The extra cost and deployment time of IoT devices have become a barrier for the adoption of IoT technologies. The proposed project will develop alternative IoT-enabling technologies that are easily deployable through tagging the objects or their environment. Such low-cost, convenient, non-intrusive methods hold great potential in making significant practical impact and expediting IoT deployment without redesigning and replacing the products already in use. The project also offers an interesting education platform for student training, undergraduate research participation, and outreach activities in the IoT domain. The goal of this proposal is to provide an alternative, convenient and non-intrusive approach that is capable of turning a vast number of existing objects in the world, without redesign, into semi-smart objects for some of the benefits promised by the IoT vision. The proposed project focuses on low-cost methods that attach RFID tags to objects or place tags in the objects' environment. It exploits the rich information embedded in the physical-layer signals from tags, such as phase, amplitude, polarization, waveform, and coupling effect to support IoT applications. The proposed research consists of two major thrusts. The first thrust investigates the IoT functions where objects are tagged. Based on the physical-layer properties of the signals transmitted from individual tags, these functions will determine the properties of the tags' carriers, including their orientations, locations, speeds, spacing, trajectories, oscillation rates, etc. The second thrust investigates the IoT functions where the objects' environment is tagged. Based on the joint physical-layer properties from a set of tags, these functions will perform object trajectory tracking and hidden content inspection in a non-intrusive manner.