1 code implementation • 30 May 2023 • Miao Ye, Chenwei Zhao, Xingsi Xue, Jinqiang Li, Hongwen Hu, Yejin Yang, Qiuxiang Jiang
Although existing SDN intelligent solution methods, which are based on deep reinforcement learning, can dynamically adapt to complex network link state changes, these methods are plagued by problems such as redundant branches, large action space, and slow agent convergence.
Combinatorial Optimization Hierarchical Reinforcement Learning +1
1 code implementation • 12 May 2023 • Jinqiang Li, Miao Ye, Linqiang Huang, Xiaofang Deng, Hongbing Qiu, Yong Wang
Second, a DRL-based data forwarding mechanism is designed in the knowledge plane.
no code implementations • 12 May 2023 • Hongwen Hu, Miao Ye, Chenwei Zhao, Qiuxiang Jiang, Yong Wang, Hongbing Qiu, Xiaofang Deng
To enable each agent to accurately understand the current network state and the status of multicast tree construction, the state space of each agent is designed based on the traffic and multicast tree status matrices, and the set of AP nodes in the network is used as the action space.
1 code implementation • 26 Dec 2022 • Miao Ye, Qinghao Zhang, Xingsi Xue, Yong Wang, Qiuxiang Jiang, Hongbing Qiu
Due to the issue that existing wireless sensor network (WSN)-based anomaly detection methods only consider and analyze temporal features, in this paper, a self-supervised learning-based anomaly node detection method based on an autoencoder is designed.
1 code implementation • 31 Jul 2022 • Chenwei Zhao, Miao Ye, Xingsi Xue, Jianhui Lv, Qiuxiang Jiang, Yong Wang
Traditional multicast routing methods have some problems in constructing a multicast tree, such as limited access to network state information, poor adaptability to dynamic and complex changes in the network, and inflexible data forwarding.
no code implementations • 19 Feb 2022 • Qinghao Zhang, Miao Ye, Hongbing Qiu, Yong Wang, Xiaofang Deng
Anomaly detection is widely used to distinguish system anomalies by analyzing the temporal and spatial features of wireless sensor network (WSN) data streams; it is one of critical technique that ensures the reliability of WSNs.