172 citations · 1.4k across the 92 of their papers we have counts for
7 papers · 1 filter
Optimizing Task-Specific Timeliness With Edge-Assisted Scheduling for Status Update
Jingzhou Sun, Lehan Wang, Zhaojun Nan +3
Intelligent real-time applications, such as video surveillance, demand intensive computation to extract status information from raw sensing data. This poses a substantial challenge…
A Grouping-based Scheduler for Efficient Channel Utilization under Age of Information Constraints
Lehan Wang, Jingzhou Sun, Yuxuan Sun +2
We consider a status information updating system where a fusion center collects the status information from a large number of sources and each of them has its own age of informatio…
Age of Information Guaranteed Scheduling for Asynchronous Status Updates in Collaborative Perception
Lehan Wang, Jingzhou Sun, Yuxuan Sun +2
We consider collaborative perception (CP) systems where a fusion center monitors various regions by multiple sources. The center has different age of information (AoI) constraints…
Data-Heterogeneous Hierarchical Federated Learning with Mobility
Tan Chen, Jintao Yan, Yuxuan Sun +3
Federated learning enables distributed training of machine learning (ML) models across multiple devices in a privacy-preserving manner. Hierarchical federated learning (HFL) is fur…
Enhanced Sliding Window Superposition Coding for Industrial Automation
Bohang Zhang, Zhaoujun Nan, Sheng Zhou +1
The introduction of 5G has changed the wireless communication industry. Whereas previous generations of cellular technology are mainly based on communication for people, the wirele…
MASS: Mobility-Aware Sensor Scheduling of Cooperative Perception for Connected Automated Driving
Yukuan Jia, Ruiqing Mao, Yuxuan Sun +2
Timely and reliable environment perception is fundamental to safe and efficient automated driving. However, the perception of standalone intelligence inevitably suffers from occlus…
SMDP-Based Dynamic Batching for Efficient Inference on GPU-Based Platforms
Yaodan Xu, Jingzhou Sun, Sheng Zhou +1
In up-to-date machine learning (ML) applications on cloud or edge computing platforms, batching is an important technique for providing efficient and economical services at scale.…