Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/20.500.12008/47543
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dc.contributor.author Autor del Castillo, Mariana-
dc.contributor.author Autor Ribeiro, Alejandro-
dc.contributor.author Autor Larroca, Federico-
dc.contributor.filiacion Filiación del Castillo Mariana, Universidad de la República (Uruguay). Facultad de Ingeniería.-
dc.contributor.filiacion Filiación Ribeiro Alejandro, University of Pennsylvania, Philadelphia, USA-
dc.contributor.filiacion Filiación Larroca Federico, Universidad de la República (Uruguay). Facultad de Ingeniería.-
dc.date.accessioned Fecha ingreso 2024-12-16T15:48:21Z-
dc.date.available Fecha disponible 2024-12-16T15:48:21Z-
dc.date.issued Año de publicación 2024-
dc.description.abstract Resumen Mobile Infrastructure on Demand (MIoD) involves deploying a mobile ad-hoc network through a team of network agents to provide communication infrastructure for another group of mobile agents serving a specific application. This study aims to determine the optimal locations for these network agents. Previous approaches have focused on maximizing graph connectivity or directly utilizing communication indicators. However, these methods often overlook the shared nature of the wireless communication medium, leading to suboptimal results. In this study, we incorporate shared access restrictions into our optimization model, addressing the inherent challenges in wireless communication. By leveraging the natural equivariance to translations and rotations of communication indicators, we employ E(n)-Equivariant Graph Neural Networks (EGNNs) to approximate the dependency of our indicator on node positions. We then use a gradient ascent algorithm to find optimal positions for the network agents. Our methodology demonstrates superior performance compared to traditional approaches, as evidenced by a higher figure of merit for the final configurations. These findings highlight the critical importance of considering the shared nature of the wireless medium for effective topology control in MIoD systems. The key contributions of this work include the incorporation of shared access restrictions into the optimization model, allowing for a more accurate representation of the problem, and the proposal of an EGNN-based Black Box Optimization approach to solve the resulting topology control problem.es
dc.description.sponsorship Financiadores Beca Doctorado CAPes
dc.format.extent Extensión 7 p.es
dc.format.mimetype Formato application/pdfes
dc.identifier.citation Citación del Castillo, M., Ribeiro, A. y Larroca, F. EGNN-based topology control in wireless mobile infrastructure on demand with shared access restrictions [en línea]. EN: GNNet ´24 : Proceedings of the 3rd GNNet Workshop on Graph Neural Networking Workshop, Los Angeles, CA, USA, 9–12 dec. 2024, pp. 46-52.es
dc.identifier.uri URI https://hdl.handle.net/20.500.12008/47543-
dc.language.iso Idioma enes
dc.publisher Editorial ACMes
dc.relation.ispartof EN GNNet ´24 : Proceedings of the 3rd GNNet Workshop on Graph Neural Networking Workshop, Los Angeles, CA, USA, 9–12 dec. 2024, pp. 46--52.es
dc.rights Derechos Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)es
dc.rights.licence Licencia Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)es
dc.subject Palabras clave Multi-agent Systemes
dc.subject Palabras clave Ad-Hoc networkses
dc.subject Palabras clave Roboticses
dc.subject Palabras clave Black box optimizationes
dc.title Título EGNN-based topology control in wireless mobile infrastructure on demand with shared access restrictions.es
dc.type Tipo de documento Ponenciaes
udelar.academic.department Departamento académico Sistemas y Control y Telecomunicacioneses
udelar.investigation.group Grupo de investigación Análisis de Redes, Tráficos y Estadísticas de Servicios (ARTES)es
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