dc.contributor.author | Antoniou, Pavlos Ch. | en |
dc.contributor.author | Pitsillides, Andreas | en |
dc.creator | Antoniou, Pavlos | en |
dc.creator | Pitsillides, Andreas | en |
dc.date.accessioned | 2019-11-13T10:38:19Z | |
dc.date.available | 2019-11-13T10:38:19Z | |
dc.date.issued | 2009 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/53563 | |
dc.description.abstract | Next 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.source | 19th International Conference on Artificial Neural Networks, ICANN 2009 | en |
dc.source.uri | https://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.subject | Backpropagation | en |
dc.subject | Neural networks | en |
dc.subject | Wireless sensor networks | en |
dc.subject | Exchange of information | en |
dc.subject | Population model | en |
dc.subject | Traffic flow | en |
dc.subject | Traffic surveys | en |
dc.subject | Traffic congestion | en |
dc.subject | Congestion control | en |
dc.subject | Next generation communication network | en |
dc.subject | Graceful performance degradations | en |
dc.subject | Lotka-Volterra competition model | en |
dc.subject | Performance evaluation | en |
dc.subject | Streaming applications | en |
dc.subject | Traffic loads | en |
dc.subject | Autonomous decentralized networks | en |
dc.subject | Autonomous infrastructures | en |
dc.subject | Biological process | en |
dc.subject | Computation burden | en |
dc.subject | Computing environments | en |
dc.subject | Constrained resources | en |
dc.subject | Decentralized networks | en |
dc.subject | Link capacities | en |
dc.subject | Lotka-volterra | en |
dc.subject | Self-adaptability | en |
dc.title | Congestion control in autonomous decentralized networks based on the lotka-volterra competition model | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1007/978-3-642-04277-5_99 | |
dc.description.volume | 5769 LNCS | en |
dc.description.issue | PART 2 | en |
dc.description.startingpage | 986 | |
dc.description.endingpage | 996 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Article | en |
dc.description.notes | <p>Conference code: 77563 | en |
dc.description.notes | Cited By :4</p> | en |
dc.source.abbreviation | Lect. Notes Comput. Sci. | en |
dc.contributor.orcid | Pitsillides, Andreas [0000-0001-5072-2851] | |
dc.gnosis.orcid | 0000-0001-5072-2851 | |