A self-adaptive routing paradigm for wireless mesh networks based on reinforcement learning (Contributo in atti di convegno)

Type
Label
  • A self-adaptive routing paradigm for wireless mesh networks based on reinforcement learning (Contributo in atti di convegno) (literal)
Anno
  • 2011-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1145/2068897.2068932 (literal)
Alternative label
  • Nurchis M. [1], Bruno R. [1], Conti M. [1], Lenzini L. [2] (2011)
    A self-adaptive routing paradigm for wireless mesh networks based on reinforcement learning
    in 14th International Symposium on Modeling Analysis and Simulation of Wireless and Mobile Systems (MSWiM 2011), Miami, Florida, USA, October 31 - November 4, 2011
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Nurchis M. [1], Bruno R. [1], Conti M. [1], Lenzini L. [2] (literal)
Pagina inizio
  • 197 (literal)
Pagina fine
  • 204 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • ID_PUMA: cnr.iit/2011-A2-048. ID Modulo Commessa 4182 - INT.P01.001.002 - 044 - Ubiquitous Internet (literal)
Note
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • [1] CNR-IIT, Pisa, Italy; [2] Dept. of Information Engineering University of Pisa, Italy (literal)
Titolo
  • A self-adaptive routing paradigm for wireless mesh networks based on reinforcement learning (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-4503-0898-4 (literal)
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. (literal)
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