Project Scope:  By tapping into next-generation wireless communication technologies such as (i) backscatter communication technology, which is highly energy-efficient, (ii) radio frequency (RF) energy harvesting technology for ambient signal energy harvesting as an alternative power supply, and (iii) the efficient design of RF signal receivers (antennas), the computation and predictive capabilities for PDD can be moved onto the farm (i.e., edge computation). Edge computation can be achieved by implementing the Tiny Machine Learning (TinyML) PDD algorithm on a processor-equipped sensor at the farm site. Therefore, this doctoral research proposal presents a novel solution through energy-efficient Edge-TinyML sensor implementation that integrates (i) backscatter wireless information and power transfer (i.e., sensor powered by energy scavenged from radio frequency signals) (WIPT) technology, (ii) efficient WIPT antenna design, and (iii) a low-cost, efficient processor for TinyML applications.

Publications and open-access (data and codes) links: