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Nonlinear Negotiation Approaches for Complex-Network Optimization: A Study Inspired by Wi-Fi Channel Assignment
At the present time, Wi-Fi networks are everywhere. They operate in unlicensed radio-frequency spectrum bands (divided in channels), which are highly congested. The purpose of this paper is to tackle the problem of channel assignment in Wi-Fi networks. To this end, we have modeled the networks as mu...
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Published in: | Group decision and negotiation 2019-02, Vol.28 (1), p.175-196 |
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creator | Marsa-Maestre, Ivan de la Hoz, Enrique Gimenez-Guzman, Jose Manuel Orden, David Klein, Mark |
description | At the present time, Wi-Fi networks are everywhere. They operate in unlicensed radio-frequency spectrum bands (divided in channels), which are highly congested. The purpose of this paper is to tackle the problem of channel assignment in Wi-Fi networks. To this end, we have modeled the networks as multilayer graphs, in a way that frequency channel assignment becomes a graph coloring problem. For a high number and variety of scenarios, we have solved the problem with two different automated negotiation techniques: a hill-climbing mediated negotiation and a simulated annealing mediated negotiation. As an upper bound reference for the performance of these two techniques, we have also solved the problem using a particle swarm optimizer. Results show that the annealer negotiator behaves as the best choice because it is able to obtain even better results than the particle swarm optimizer in the most complex scenarios under study, with running times one order of magnitude below. Moreover, we study how different properties of the network layout affect to the performance gain that the annealer is able to obtain with respect to the particle swarm optimizer. Finally, we show how the different strategic behavior of the participants affects the results. |
doi_str_mv | 10.1007/s10726-018-9600-z |
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They operate in unlicensed radio-frequency spectrum bands (divided in channels), which are highly congested. The purpose of this paper is to tackle the problem of channel assignment in Wi-Fi networks. To this end, we have modeled the networks as multilayer graphs, in a way that frequency channel assignment becomes a graph coloring problem. For a high number and variety of scenarios, we have solved the problem with two different automated negotiation techniques: a hill-climbing mediated negotiation and a simulated annealing mediated negotiation. As an upper bound reference for the performance of these two techniques, we have also solved the problem using a particle swarm optimizer. Results show that the annealer negotiator behaves as the best choice because it is able to obtain even better results than the particle swarm optimizer in the most complex scenarios under study, with running times one order of magnitude below. Moreover, we study how different properties of the network layout affect to the performance gain that the annealer is able to obtain with respect to the particle swarm optimizer. Finally, we show how the different strategic behavior of the participants affects the results.</description><identifier>ISSN: 0926-2644</identifier><identifier>EISSN: 1572-9907</identifier><identifier>DOI: 10.1007/s10726-018-9600-z</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Assignment ; Biological and Physical Anthropology ; Business and Management ; Climbing ; Graph coloring ; Graphs ; Layout ; Negotiation ; Optimization ; Radio ; Satellites</subject><ispartof>Group decision and negotiation, 2019-02, Vol.28 (1), p.175-196</ispartof><rights>Springer Nature B.V. 2018</rights><rights>Group Decision and Negotiation is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c348t-31ac2996e088a61414438be00a688610ccf52d68a881d6abc1a2fc52550f733e3</citedby><cites>FETCH-LOGICAL-c348t-31ac2996e088a61414438be00a688610ccf52d68a881d6abc1a2fc52550f733e3</cites><orcidid>0000-0002-8448-5900 ; 0000-0003-4837-3837 ; 0000-0001-5403-8467 ; 0000-0002-5529-2851 ; 0000-0002-1645-8476</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2136795691/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2136795691?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,11688,12847,27924,27925,33223,36060,44363,74895</link.rule.ids></links><search><creatorcontrib>Marsa-Maestre, Ivan</creatorcontrib><creatorcontrib>de la Hoz, Enrique</creatorcontrib><creatorcontrib>Gimenez-Guzman, Jose Manuel</creatorcontrib><creatorcontrib>Orden, David</creatorcontrib><creatorcontrib>Klein, Mark</creatorcontrib><title>Nonlinear Negotiation Approaches for Complex-Network Optimization: A Study Inspired by Wi-Fi Channel Assignment</title><title>Group decision and negotiation</title><addtitle>Group Decis Negot</addtitle><description>At the present time, Wi-Fi networks are everywhere. 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subjects | Assignment Biological and Physical Anthropology Business and Management Climbing Graph coloring Graphs Layout Negotiation Optimization Radio Satellites |
title | Nonlinear Negotiation Approaches for Complex-Network Optimization: A Study Inspired by Wi-Fi Channel Assignment |
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