activity
20192022
most citedJoint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System

64 citations · 67 across the 3 of their papers we have counts for

collaborators

5 papers

cs.NE202264 cited

Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System

Li Dong, Zhibin Liu, Feibo Jiang +1

Unmanned aerial vehicles (UAVs) can be applied in many Internet of Things (IoT) systems, e.g., smart farms, as a data collection platform. However, the UAV-IoT wireless channels ma…

cs.DC20203 cited

Distributed Resource Scheduling for Large-Scale MEC Systems: A Multi-Agent Ensemble Deep Reinforcement Learning with Imitation Acceleration

Feibo Jiang, Li Dong, Kezhi Wang +2

We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large…

eess.SP2020

AI Driven Heterogeneous MEC System with UAV Assistance for Dynamic Environment -- Challenges and Solutions

Feibo Jiang, Kezhi Wang, Li Dong +3

By taking full advantage of Computing, Communication and Caching (3C) resources at the network edge, Mobile Edge Computing (MEC) is envisioned as one of the key enablers for the ne…

cs.LG2020

Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks

Feibo Jiang, Kezhi Wang, Li Dong +2

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet of things (IoT) users, by optimizing offloading decision, t…

eess.SP2019

Deep Learning Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks

Feibo Jiang, Kezhi Wang, Li Dong +3

In this paper, we consider a hybrid mobile edge computing (H-MEC) platform, which includes ground stations (GSs), ground vehicles (GVs) and unmanned aerial vehicle (UAVs), all with…