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- W2046164077 abstract "Classical routing protocols for WMNs are typically designed to achieve specific target objectives (e.g., maximum throughput), and they offer very limited flexibility. As a consequence, more intelligent and adaptive mesh networking solutions are needed to obtain high performance in diverse network conditions. To this end, we propose a reinforcement learning-based routing framework that allows each mesh device to dynamically select at run time a routing protocol from a pre-defined set of routing options, which provides the best performance. The most salient advantages of our solution are: i) it can maximize routing performance considering different optimization goals, ii) it relies on a compact representation of the network state and it does not need any model of its evolution, and iii) it efficiently applies Q-learning methods to guarantee convergence of the routing decision process. Through extensive ns-2 simulations we show the superior performance of the proposed routing approach in comparison with two alternative routing schemes." @default.
- W2046164077 created "2016-06-24" @default.
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- W2046164077 date "2011-10-31" @default.
- W2046164077 modified "2023-10-03" @default.
- W2046164077 title "A self-adaptive routing paradigm for wireless mesh networks based on reinforcement learning" @default.
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- W2046164077 doi "https://doi.org/10.1145/2068897.2068932" @default.
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