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dc.contributor.authorKarami, Amin
dc.date.accessioned2015-11-15T11:10:13Z
dc.date.issued2015-10-01
dc.identifier.citationKarami, A. ACCPndn : adaptive congestion control protocol in named data networking by learning capacities using optimized time-lagged feedforward neural network. "Journal of network and computer applications", 01 Octubre 2015, vol. 56, p. 1-18.
dc.identifier.issn1084-8045
dc.identifier.urihttp://hdl.handle.net/2117/79277
dc.description.abstractNamed Data Networking (NDN) is a promising network architecture being considered as a possible replacement for the current IP-based Internet infrastructure. However, NDN is subject to congestion when the number of data packets that reach one or various routers in a certain period of time is so high than its queue gets overflowed. To address this problem many congestion control protocols have been proposed in the literature which, however, they are highly sensitive to their control parameters as well as unable to predict congestion traffic well enough in advance. This paper develops an Adaptive Congestion Control Protocol in NON (ACCPndn) by learning capacities in two phases to control congestion traffics before they start impacting the network performance. In the first phase - adaptive training - we propose a Time-Lagged Feedforward Network (TLFN) optimized by hybridization of particle swarm optimization and genetic algorithm to predict the source of congestion together with the amount of congestion. In the second phase -fuzzy avoidance- we employ a non-linear fuzzy logic-based control system to make a proactive decision based on the outcomes of first phase in each router per interface to control and/or prevent packet drop well enough in advance. Extensive simulations and results show that ACCPndn sufficiently satisfies the applied performance metrics and outperforms two previous proposals such as NACK and HoBHIS in terms of the minimal packet drop and high-utilization (retrying alternative paths) in bottleneck links to mitigate congestion traffics.
dc.format.extent18 p.
dc.language.isoeng
dc.subject.lcshFuture internet
dc.subject.otherNamed data networking
dc.subject.otherCongestion control
dc.subject.otherTime-lagged feedforward network
dc.subject.otherParticle swarm optimization
dc.subject.otherGenetic algorithm
dc.subject.otherFuzzy set
dc.subject.otherPARTICLE SWARM OPTIMIZATION
dc.subject.otherINFORMATION-CENTRIC NETWORKING
dc.subject.otherCACHE POLLUTION ATTACKS
dc.subject.otherGENETIC ALGORITHM
dc.subject.otherTRAFFIC PREDICTION
dc.subject.otherSERIES
dc.subject.otherHYBRID
dc.subject.otherMECHANISM
dc.subject.otherSYSTEMS
dc.subject.otherMODEL
dc.titleACCPndn : adaptive congestion control protocol in named data networking by learning capacities using optimized time-lagged feedforward neural network
dc.typeArticle
dc.subject.lemacInternet del futur
dc.identifier.doi10.1016/j.jnca.2015.05.017
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S1084804515001265
dc.rights.accessRestricted access - publisher's policy
drac.iddocument16979301
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
upcommons.citation.authorKarami, A.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameJournal of network and computer applications
upcommons.citation.volume56
upcommons.citation.startingPage1
upcommons.citation.endingPage18


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