5 papers
Reinforcement Learning for Delivery Drone-Based Participatory Sensing in Dynamic Environments
Xin Ouyang, Songxin Lei, Xusen Guo +3
Using Unmanned Aerial Vehicle (UAV) for urban sensing has emerged as a powerful paradigm to monitor the status of the city, e.g., air quality and noise levels, through agile aerial…
Double-Diffusion: Balancing Speed, Accuracy, and Uncertainty in Probabilistic Forecasting for Urban Sensor Networks
Hanlin Dong, Arian Prabowo, Hao Xue +4
Urban sensor networks need forecasts that are accurate, carry useful uncertainty, and refresh fast enough to act on as new readings arrive. These goals conflict: deterministic mode…
Language-Grounded Multi-Agent Planning for Personalized and Fair Participatory Urban Sensing
Xusen Guo, Mingxing Peng, Hongliang Lu +3
Participatory urban sensing leverages human mobility for large-scale urban data collection, yet existing methods typically rely on centralized optimization and assume homogeneous p…
Bayesian-Driven Graph Reasoning for Active Radio Map Construction
Wenlihan Lu, Shijian Gao, Miaowen Wen +4
With the emergence of the low-altitude economy, radio maps have become essential for ensuring reliable wireless connectivity to aerial platforms. Autonomous aerial agents are commo…
Aligning Beam with Imbalanced Multi-modality: A Generative Federated Learning Approach
Jiahui Liang, Miaowen Wen, Shuoyao Wang +2
As vehicle intelligence advances, multi-modal sensing-aided communication emerges as a key enabler for reliable Vehicle-to-Everything (V2X) connectivity through precise environment…