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Latency filtering for Q-routing on wireless networks

机译：无线网络上Q路由的延迟过滤

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摘要

Q-routing is inspired by Q-learning, a reinforcement learning algorithm. Originally, it uses latency as routing metric. But, latency can be difficult to estimate especially in a noisy wireless environment. In this paper, we propose to filter the latency measure with a moving average, in order to improve the quality of service metrics such as packet delivery ratio and average delay. We compare our modification to the original Q-routing and use OLSRv2 as reference routing protocol. We observe an improvement of the average delivery time on a wireless grid of 3 % compared to the original Q-routing. On our mobility scenario, the number of routes changes is at least twice lower (from 6 to 3 route changes between the two approaches in this scenario). The gain on QoS metrics depends mainly on the speed of the nodes. These improvements are obtained without making Q-routing more complex as a moving average is added. We provide all the materials to conduct reproducible research on our public git repository.
机译：Q-Routing受到Q-Learning，加强学习算法的启发。最初，它使用延迟作为路由度量。但是，延迟可能难以估计，特别是在嘈杂的无线环境中。在本文中，我们建议以移动平均线过滤延迟测量，以提高数据包传递比和平均延迟等服务度量的质量。我们将我们的修改与原始Q路由进行比较，并使用OLSRV2作为参考路由协议。与原始Q路由相比，我们遵守3％的无线网格上的平均交付时间的改进。在我们的移动方案中，路线的数量更改至少两倍（从这两个方案中的两种方法之间的6到3路线）。 QoS指标上的增益主要取决于节点的速度。获得这些改进而不使Q-Routing更复杂，因为添加了移动平均值。我们提供所有材料对我们的公共GIT存储库进行可重复的研究。

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