Paper Info
Title
A self-adaptive routing paradigm for wireless mesh networks based on reinforcement learning
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.
Year
DOI
Venue
2011
10.1145/2068897.2068932
MSWiM
Keywords
Field
DocType
self-adaptive routing paradigm,classical routing protocol,wireless mesh network,proposed routing approach,best performance,diverse network condition,routing protocol,adaptive mesh networking solution,mesh device,routing decision process,high performance,reinforcement learning,superior performance,adaptive routing
Link-state routing protocol,Multipath routing,Dynamic Source Routing,Static routing,Enhanced Interior Gateway Routing Protocol,Computer science,Policy-based routing,Computer network,Wireless Routing Protocol,Zone Routing Protocol,Distributed computing
Conference
Citations 
PageRank 
References 
2
0.42
12
Authors
4
Name
Order
Citations
PageRank
Maddalena Nurchis1685.75
Raffaele Bruno2123290.09
Marco Conti31490114.70
Luciano Lenzini4100081.89