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dc.contributor.authorAntoniou, Pavlos Ch.en
dc.contributor.authorPitsillides, Andreasen
dc.creatorAntoniou, Pavlosen
dc.creatorPitsillides, Andreasen
dc.date.accessioned2019-11-13T10:38:19Z
dc.date.available2019-11-13T10:38:19Z
dc.date.issued2009
dc.identifier.issn0302-9743
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/53563
dc.description.abstractNext generation communication networks are moving towards autonomous infrastructures that are capable of working unattended under dynamically changing conditions. The new network architecture involves interactions among unsophisticated entities which may be characterized by constrained resources. From this mass of interactions collective unpredictable behavior emerges in terms of traffic load variations and link capacity fluctuations, leading to congestion. Biological processes found in nature exhibit desirable properties e.g. self-adaptability and robustness, thus providing a desirable basis for such computing environments. This study focuses on streaming applications in sensor networks and on how congestion can be prevented by regulating the rate of each traffic flow based on the Lotka-Volterra population model. Our strategy involves minimal exchange of information and computation burden and is simple to implement at the individual node. Performance evaluations reveal that our approach achieves adaptability to changing traffic loads, scalability and fairness among flows, while providing graceful performance degradation as the offered load increases. © 2009 Springer Berlin Heidelberg.en
dc.source19th International Conference on Artificial Neural Networks, ICANN 2009en
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-70450158438&doi=10.1007%2f978-3-642-04277-5_99&partnerID=40&md5=1d80d65a551b1333a85fd3332f30f763
dc.subjectBackpropagationen
dc.subjectNeural networksen
dc.subjectWireless sensor networksen
dc.subjectExchange of informationen
dc.subjectPopulation modelen
dc.subjectTraffic flowen
dc.subjectTraffic surveysen
dc.subjectTraffic congestionen
dc.subjectCongestion controlen
dc.subjectNext generation communication networken
dc.subjectGraceful performance degradationsen
dc.subjectLotka-Volterra competition modelen
dc.subjectPerformance evaluationen
dc.subjectStreaming applicationsen
dc.subjectTraffic loadsen
dc.subjectAutonomous decentralized networksen
dc.subjectAutonomous infrastructuresen
dc.subjectBiological processen
dc.subjectComputation burdenen
dc.subjectComputing environmentsen
dc.subjectConstrained resourcesen
dc.subjectDecentralized networksen
dc.subjectLink capacitiesen
dc.subjectLotka-volterraen
dc.subjectSelf-adaptabilityen
dc.titleCongestion control in autonomous decentralized networks based on the lotka-volterra competition modelen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1007/978-3-642-04277-5_99
dc.description.volume5769 LNCSen
dc.description.issuePART 2en
dc.description.startingpage986
dc.description.endingpage996
dc.author.faculty002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeArticleen
dc.description.notes<p>Conference code: 77563en
dc.description.notesCited By :4</p>en
dc.source.abbreviationLect. Notes Comput. Sci.en
dc.contributor.orcidPitsillides, Andreas [0000-0001-5072-2851]
dc.gnosis.orcid0000-0001-5072-2851


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