7 Papers
Yuhang Wang is an academic researcher from Chongqing University of Posts and Telecommunications. The author has contributed to research in topics: Computer science & Software deployment. The author has an hindex of 1, co-authored 1 publications.
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Papers
Efficient Privacy-Preserving Dual Authentication and Key Agreement Scheme for Secure V2V Communications in an IoV Paradigm
TL;DR: This paper focuses on the security and privacy-preserving by developing a dual authentication scheme for IoV according to its different scenarios, and proves the correctness of this scheme using the Burrows–Abadi–Needham (BAN) logic.
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Efficient Resource Allocation for Multi-Beam Satellite-Terrestrial Vehicular Networks: A Multi-Agent Actor-Critic Method With Attention Mechanism
TL;DR: A multi-agent actor-critic method with attention mechanism is proposed to allocate resources for vehicles with strict delay requirements and minimum bandwidth consumption, where all the agents can well cooperative to achieve efficient resource allocation on-demand for the vehicles under strictly limited bandwidth resources.
46
Intelligent optimization under the makespan constraint: Rapid evaluation mechanisms based on the critical machine for the distributed flowshop group scheduling problem
TL;DR: Wang et al. as discussed by the authors proposed a cooperative iterated greedy algorithm (CIG) combining two rapid evaluation methods, in which inter-group and intra-group neighborhood search strategies are proposed to enhance the search depth and breadth.
28
$Q_{C}-DQN$: A Novel Constrained Reinforcement Learning Method for Computation Offloading in Multi-access Edge Computing
Shen Zhuang,Chengxi Gao,Ying He,F. Richard Yu,Yuhang Wang,Weike Pan,Zhong Ming +6 more
- 18 Jul 2022
TL;DR: This article presents a novel framework for MEC networks with unmanned aerial vehicles (UAVs) and intelligent reflecting surfaces (IRSs) to facilitate computation offloading with delay constraints and proposes a novel constrained reinforcement learning method with a dynamic balance mechanism named $Q_{c}-DQN$.
Meta-Hierarchical Reinforcement Learning (MHRL)-Based Dynamic Resource Allocation for Dynamic Vehicular Networks
TL;DR: This paper model the dynamics of the vehicular environment as a series of related Markov Decision Processes (MDPs), and it combines hierarchical reinforcement learning with meta-learning, which makes the proposed framework quickly adapt to a new environment by only fine-tuning the top-level master network.