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

Mubashir Husain Rehmani 1 Aline Carneiro Viana 2 Hicham Khalife 3 Serge Fdida 1
2 ASAP - As Scalable As Possible: foundations of large scale dynamic distributed systems
UR1 - Université de Rennes 1, Inria Saclay - Ile de France, INSA - Institut National des Sciences Appliquées, CNRS - Centre National de la Recherche Scientifique : UMR
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 three 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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https://hal.inria.fr/inria-00596224
Contributor : Mubashir Husain Rehmani <>
Submitted on : Sunday, July 31, 2011 - 6:36:23 PM
Last modification on : Tuesday, May 14, 2019 - 11:02:19 AM
Document(s) archivé(s) le : Thursday, March 30, 2017 - 1:39:55 PM

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  • HAL Id : inria-00596224, version 3

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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. [Research Report] RR-7628, INRIA. 2011. ⟨inria-00596224v3⟩

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