Yixuan Luo
Papers
2
Total Citations
10
H-Index
2
About
Yixuan Luo is a rising researcher at the forefront of mobile crowdsensing and human–robot collaboration, with a focus on intelligent task allocation in pervasive computing environments. Her work addresses the critical challenge of coordinating heterogeneous agents—both human-operated devices and autonomous machines—to efficiently collect and process sensor data across dynamic, large-scale settings. In her highly cited 2022 paper, "Multi-agent mobile crowdsensing by pervasive machines," she introduced a robust task allocation framework that optimizes resource distribution under uncertainty, achieving notable impact with 5 citations. Building on this foundation, her 2024 study, "ContinuousSensing," proposed a novel algorithm enabling seamless task migration between human and robotic collaborators, significantly enhancing adaptability and coverage in real-time sensing applications. Luo’s contributions are particularly valuable for smart city infrastructure, environmental monitoring, and disaster response, where reliable, continuous data collection is paramount. Her work not only advances algorithmic theory but also provides practical solutions for next-generation cyber-physical systems. As a young scholar, Yixuan Luo is already shaping the future of collaborative sensing, and her research continues to inspire new directions in multi-agent systems and human–robot interaction.
Research Focus
Key Achievements
Top Papers
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- 2