Multi-agent nonlinear negotiation for Wi-Fi channel assignment
Autores
Hoz de la Hoz, Enrique de la; Marsá Maestre, Iván; Giménez Guzmán, José Manuel; Orden Martín, David; Klein, MarkEditor
International Foundation for Autonomous Agents and Multiagent Systems
Fecha de publicación
2017-05-08Fecha fin de embargo
2017-11-12Filiación
Universidad de Alcalá. Departamento de Automática; Universidad de Alcalá. Departamento de Física y MatemáticasPatrocinadores
Ministerio de Economía y Competitividad
Cita bibliográfica
Hoz, E., Marsa-Maestre, I., Gimenez-Guzman, J.M., Orden, D. & Klein, M., 2017, "Multi-agent nonlinear negotiation for Wi-Fi channel assignment", Proceedings of the Sixteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017).
Descripción
AAMAS 2017 - Sixteenth International Conference on Autonomous Agents and Multiagent Systems, 08/05/2017-12/05/2017, Sao Paulo, Brasil.
Proyectos
info:eu-repo/grantAgreement/MINECO//TIN2016-80622-P/ES/Dynamic Network Agreement: negociaciones estructurales en redes complejas/DNA
info:eu-repo/grantAgreement/MINECO//TIN 2014-61627-EXP/ES/DIVIDE AND NOT CONQUER-COMPORTAMIENTOS EMERGENTES EN REDES COMPLEJAS EGOISTAS
info:eu-repo/grantAgreement/MINECO//MTM2014-54207-P/ES/COMBINATORIA Y COMPLEJIDAD DE ESTRUCTURAS GEOMETRICAS DISCRETAS
Tipo de documento
info:eu-repo/semantics/conferenceObject
Versión
info:eu-repo/semantics/acceptedVersion
Derechos
International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org)
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Derechos de acceso
info:eu-repo/semantics/openAccess
Resumen
Optimizing resource use in complex networks with self-interested participants (e.g. transportation networks, electric grids, Internet systems) is a challenging and increasingly critical real-world problem. We propose an approach for solving this problem based on multi-agent nonlinear negotiation, and demonstrate it in the context of Wi-Fi channel assignment.
We compare the performance of our proposed approaches with a complete information optimizer based on particle swarms, together with the \emph{de facto} heuristic technique based on using the least congested channel. We have evaluated all these techniques in a wide range of settings, including randomly generated scenarios and real-world ones.
Our experiments show that our approach outperforms the rest of techniques in terms of social welfare.
The particle swarm optimizer is the only technique whose performance is close to ours, but its computation cost is much higher. Finally, we also study the effect of some graphs metrics on the gain that our approach can achieve.
Ficheros en el ítem
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Multi-agent_AAMAS_2017.pdf | 1.230Mb |
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Multi-agent_AAMAS_2017.pdf | 1.230Mb |
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