• Explore
    • Contact Us
  • Faculty
  • Research
    • Research Areas
    • Research Centers
  • Graduate Degrees
    • Computer Science Programs
    • Current Graduate Students
  • Undergraduate Degrees
  • News & Events
    • News
    • Seminar Series
    • Distinguished Lecture Series
    • Research Showcase
  • Apply Now
    • Undergraduate Admissions
    • Graduate Admissions
    • Faculty Candidates

Marco Levorato Receives NSF Grant for Mobile Autonomous Systems (MAS) Research

August 29, 2021

Assistant Professor of Computer Science Marco Levorato was awarded $415,000 from the National Science Foundation (NSF) for his grant, “Reliable Task Offloading in Mobile Autonomous Systems Through Semantic MU-MIMO Control.”

“Mobile autonomous systems (MASs) such as self-driving vehicles and drones have a pivotal role in critical applications such as urban mobility, precision agriculture and remote surveillance,” explains the grant abstract. “To achieve their tasks, MASs increasingly rely on high-throughput low-latency streaming of computer vision tasks (e.g., object detection) to edge servers. However, ephemeral environmental factors such as blockages, congestion and fading may erratically interrupt the flow of tasks to the edge servers.”

This project aims to develop applications that are “resilient by design” without compromising task accuracy. The three-year project is a collaboration with Francesco Restuccia, assistant professor of electrical and computer engineering at Northeastern University.

“I partnered with Francesco as he is a lead expert in wireless systems,” says Levorato, “and has access (and expertise to use) the large-scale wireless testbed (Colosseum, funded by NSF) at Northeastern.”

The researchers will be developing a drone-based prototype and performing large-scale data collection campaigns at Northeastern. They will also use a drone experimental testbed at UC Irvine to collect wireless/multimedia data to train the algorithms and to perform extensive testing and performance evaluation.

The core goal of this project, as outlined in the abstract, is to design and evaluate “novel techniques for hardware-based semantic-driven joint optimization of multimedia compression strategies and MU-MIMO [Multi-User Multiple-Input and Multiple-Output] transmissions in the context of resource-limited wireless systems.”

— Shani Murray

« Computer Science Team Presents Research on the ‘Security of Deep Learning based Automated Lane Centering under Physical-World Attack’ at USENIX Security Symposium
Wired: “A Bad Solar Storm Could Cause an ‘Internet Apocalypse'” (Sangeetha Abdu Jyothi quoted) »

Latest news

  • Wired: “Google Has a Plan to Stop Its New AI From Being Dirty and Rude” (Sameer Singh quoted) May 12, 2022
  • NSF Announces 2022 Graduate Research Fellows May 2, 2022
  • Alumni Spotlight: Ludovico Verniani ’21 Rewards Adventure With Quadra App April 28, 2022
  • UCI Celebration of Teaching Honors Three ICS Faculty Members April 26, 2022
  • UCI Machine Learning Repository to Host 2022 ML Hackathon April 26, 2022
  • © 2022 UC Regents
  • Feedback
  • Privacy Policy