Wireless mesh network (WMN) is one of the most promising technologies for Internet of Things (IoT) applications because of its self-adaptive and self-organization nature. To meet different performance requirements on communications in WMNs, traditional approaches always have to program flow control strategies in an explicit way. In this case, the performance of WMNs will be significantly affected by the dynamic properties of underlying networks in real applications. With providing a more flexible solution in mind, in this article, for the first time, we present how we can apply emerging Deep Reinforcement Learning (DRL) on communication flow control in WMNs. Moreover, different from a general DRL based networking solution, in which the network properties are pre-defined, we leverage the adaptive nature of WMNs and propose a self-adaptive DRL approach. Specifically, our method can reconstruct a WMN during the training of a DRL model. In this way, the trained DRL model can capture more properties of WMNs and achieve better performance. As a proof of concept, we have implemented our method with a self-adap-tive Deep Q-learning Network (DQN) model. The evaluation results show that the presented solution can significantly improve the communication performance of data flows in WMNs, compared to a static benchmark solution.

中文翻译：

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无线网状网络中通信流量控制的深度强化学习


无线网状网络（WMN）因其自适应和自组织性质而成为物联网（IoT）应用中最有前途的技术之一。为了满足无线Mesh网络中不同的通信性能要求，传统的方法总是必须以显式的方式编写流量控制策略。在这种情况下，WMN的性能将受到实际应用中底层网络的动态特性的显着影响。考虑到提供更灵活的解决方案，在本文中，我们首次介绍如何将新兴的深度强化学习 (DRL) 应用于 WMN 中的通信流控制。此外，与预定义网络属性的一般基于 DRL 的网络解决方案不同，我们利用 WMN 的自适应特性，提出了一种自适应 DRL 方法。具体来说，我们的方法可以在 DRL 模型的训练期间重建 WMN。这样，经过训练的 DRL 模型可以捕获 WMN 的更多属性并获得更好的性能。作为概念证明，我们使用自适应深度 Q 学习网络 (DQN) 模型来实现我们的方法。评估结果表明，与静态基准解决方案相比，所提出的解决方案可以显着提高WMN中数据流的通信性能。