Miao Pan
University of Houston
259 Papers
1.2K Citations
Miao Pan is an academic researcher from University of Houston. The author has contributed to research in topics: Computer science & Cognitive radio. The author has an hindex of 32, co-authored 254 publications. Previous affiliations of Miao Pan include University of Florida & Dalian University of Technology.
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Papers
Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach
TL;DR: A selective model aggregation approach is proposed, where “fine” local DNN models are selected and sent to the central server by evaluating the local image quality and computation capability, and demonstrated to outperform the original federated averaging approach in terms of accuracy and efficiency.
Optimal Power Management of Residential Customers in the Smart Grid
TL;DR: This work mathematically formulate this problem as a stochastic optimization problem and approximately solve it by using the Lyapunov optimization approach, and has found a good tradeoff between cost saving and storage capacity.
252
Optimal VNF Placement via Deep Reinforcement Learning in SDN/NFV-Enabled Networks
TL;DR: Evaluation results show that DDQN-VNFPA can get improved network performance in terms of the reject number and reject ratio of Service Function Chain Requests, throughput, end-to-end delay, VNFI running time and load balancing compared with the algorithms in existing literatures.
236
IoT Enabled UAV: Network Architecture and Routing Algorithm
TL;DR: A layered UAV swarm network architecture is proposed and an optimal number of UAVs is analyzed and a low latency routing algorithm (LLRA) is designed based on the partial location information and the connectivity of the network architecture.
192
Decentralized Coordination of Energy Utilization for Residential Households in the Smart Grid
TL;DR: An online control algorithm, called Lyapunov-based cost minimization algorithm (LCMA), is developed, which jointly considers the energy management and demand management decisions and can effectively reduce the total energy cost in the neighborhood.
188