Xu Tao, a graduate student in the Pigman College of Engineering , was one of 45 scholars from across the nation selected to attend the competitive 2024 CPS Rising Stars Workshop, hosted by the University of Virginia School of Engineering and Applied Science.
Tao is a Ph.D. candidate in Department of Computer Science, under the supervision of Simone Silvestri, Ph.D. She previously worked as a researcher at the LINKS Foundation in Italy from 2018 to 2021, and holds a Master of Science in computer engineering from Politecnico di Torino, Italy, earned in 2018. Her research interest lies in harnessing the potential of Internet of Things, Cyber-Physical System and LPWAN Network to revolutionize smart agriculture.
This CPS Rising Stars Workshop aims to identify and mentor outstanding Ph.D. students and postdocs who are interested in pursuing academic careers in Cyber-Physical Systems (CPS) related areas. CPS are engineered systems that are built from, and depend upon, the seamless integration of computation and physical components. The workshop program committee selects CPS Rising Stars based on research excellence and academic leadership potential.
Rapid advancements in Cyber-Physical Systems (CPS) have revolutionized various domains by seamlessly integrating physical and computational elements. However, limited internet coverage in remote areas hampers CPS deployment in vital sectors like agriculture. To address this challenge, we consider utilizing Unmanned Aerial Vehicles (UAVs) to extend the network connectivity and propose efficient data collection techniques for high precision crop monitoring. In addition, we explore the potential of LoRa, an emerging low-power, long-range communication technology as an ideal solution for communication in large-scale rural farms. Our focus lies in utilizing LoRa for image transmission, facilitating a broader of machine learning-based applications, such as crop diseases monitoring and insects detection without requiring extensive and costly communication infrastructure. Furthermore, we introduce novel and efficient image processing methods at the end device to reduce the amount of data transmission over LoRa, resulting in decreased transmission latency and high disease detection accuracy.
