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A self-adaptive routing paradigm for wireless mesh networks based on reinforcement learning

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.

14th International Symposium on Modeling Analysis and Simulation of Wireless and Mobile Systems (MSWiM 2011), Miami Beach (FL - USA), 2011

Autori esterni: Luciano Lenzini (Dept. of Information Engineering University of Pisa, Italy)
Autori IIT:

Maddalena Nurchis

Foto di Maddalena Nurchis

Tipo: Articolo in Atti di convegno internazionale con referee
Area di disciplina: Information Technology and Communication Systems
Da pagina 197 a pagina 204