SURF: A Distributed Channel Selection Strategy for Data Dissemination in Multi-Hop Cognitive Radio Networks

Abstract : In this paper, we propose an intelligent and distributed channel selection strategy for efficient data dissemination in multi-hop cognitive radio network. Our strategy, SURF, classifies the available channels and uses them efficiently to increase data dissemination reliability in multi-hop cognitive radio networks. The classification is done on the basis of primary radio unoccupancy and of the number of cognitive radio neighbors using the channels. Through extensive NS-2 simulations, we study the performance of SURF compared to four related approaches. Simulation results confirm that our approach is effective in selecting the best channels for efficient communication (in terms of less primary radio interference) and for highest dissemination reachability in multi-hop cognitive radio networks.
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Article dans une revue
Computer Communications, Elsevier, 2013, 36 (10-11), pp.1172-1185. 〈10.1016/j.comcom.2013.03.005〉
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https://hal.inria.fr/hal-01112622
Contributeur : Aline Carneiro Viana <>
Soumis le : mardi 3 février 2015 - 12:17:30
Dernière modification le : mercredi 28 novembre 2018 - 01:24:34

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Mubashir Husain Rehmani, Aline Carneiro Viana, Hicham Khalife, Serge Fdida. SURF: A Distributed Channel Selection Strategy for Data Dissemination in Multi-Hop Cognitive Radio Networks. Computer Communications, Elsevier, 2013, 36 (10-11), pp.1172-1185. 〈10.1016/j.comcom.2013.03.005〉. 〈hal-01112622〉

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