Sánchez Iborra, Ramón Jesús

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Sánchez Iborra, Ramón Jesús

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Sánchez Iborra, Ramón Jesús

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Ingeniería de la Información y las Comunicaciones

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Open Access
Calidad de Experiencia (QoE): generalidades y casos de estudio
(2026-04-13) Sánchez Iborra, Ramón Jesús; Ingeniería de la Información y las Comunicaciones; Facultad de Informática
Sesión formativa sobre el concepto de Calidad de Experiencia (Quality of Experience, QoE) en servicios multimedia transmitidos a través de redes de telecomunicaciones.
Open Access
SURROGATES: Virtual OBUs to Foster 5G Vehicular Services
(2019) Santa Lozano, J.; Fernández Ruiz, Pedro Javier; Ortiz Murillo, J.; Skarmeta Gomez, A. F.; Sánchez Iborra, Ramón Jesús; Ingeniería de la Información y las Comunicaciones
Open Access
Calidad del Servicio en Redes Inalámbricas
(2026-04-13) Sánchez Iborra, Ramón Jesús; Ingeniería de la Información y las Comunicaciones; Facultad de Informática
Material formativo que presenta el concepto de Calidad de Servicio (Quality of Service, QoS) específicamente en redes de comunicaciones inalámbricas.
Open Access
Enhancing Extensive and Remote LoRa Deployments through MEC-Powered Drone Gateways
(MDPI, 2020-07-23) Gallego Madrid, Jorge; Molina Zarca, Alejandro; Bernal Bernabé, Jorge; Santa, José; Ruiz Martínez, Pedro Miguel; Skarmeta Gómez, Antonio F.; Sánchez Iborra, Ramón Jesús; Ingeniería de la Información y las Comunicaciones
The distribution of Internet of Things (IoT) devices in remote areas and the need for network resilience in such deployments is increasingly important in smart spaces covering scenarios, such as agriculture, forest, coast preservation, and connectivity survival against disasters. Although Low-Power Wide Area Network (LPWAN) technologies, like LoRa, support high connectivity ranges, communication paths can suﬀer from obstruction due to orography or buildings, and large areas are still diﬃcult to cover with wired gateways, due to the lack of network or power infrastructure. The proposal presented herein proposes to mount LPWAN gateways in drones in order to generate airborne network segments providing enhanced connectivity to sensor nodes wherever needed. Our LoRa-drone gateways can be used either to collect data and then report them to the back-oﬃce directly, or store-carry-and-forward data until a proper communication link with the infrastructure network is available. The proposed architecture relies on Multi-Access Edge Computing (MEC) capabilities to host a virtualization platform on-board the drone, aiming at providing an intermediate processing layer that runs Virtualized Networking Functions (VNF). This way, both preprocessing or intelligent analytics can be locally performed, saving communications and memory resources. The contribution includes a system architecture that has been successfully validated through experimentation with a real test-bed and comprehensively evaluated through computer simulation. The results show significant communication improvements employing LoRa-drone gateways when compared to traditional fixed LoRa deployments in terms of link availability and covered areas, especially in vast monitored extensions, or at points with diﬃcult access, such as rugged zones.
Open Access
Laboratorio conectado: experimentación y despliegue de arquitecturas Cloud-IoT
(2026-04-13) Sánchez Iborra, Ramón Jesús; Ingeniería de la Información y las Comunicaciones; Facultad de Informática
Este curso presenta una introducción al Internet de las Cosas y entornos cloud. Se presenta un montaje hardware con Arduino que permite presentar datos e interactuar con ellos tanto en local, como en una plataforma cloud.
Open Access
Machine learning-based zero-touch network and service management: a survey
(Elsevier KeAi Communications, 2022-04) Gallego Madrid, Jorge; Ruiz Martínez, Pedro Miguel; Skarmeta Gómez, Antonio F.; Sánchez Iborra, Ramón Jesús; Ingeniería de la Información y las Comunicaciones
The exponential growth of mobile applications and services during the last years has challenged the existing network infrastructures. Consequently, the arrival of multiple management solutions to cope with this explosion along the end-to-end network chain has increased the complexity in the coordinated orchestration of different segments composing the whole infrastructure. The Zero-touch Network and Service Management (ZSM) concept has recently emerged to automatically orchestrate and manage network resources while assuring the Quality of Experience (QoE) demanded by users. Machine Learning (ML) is one of the key enabling technologies that many ZSM frameworks are adopting to bring intelligent decision making to the network management system. This paper presents a comprehensive survey of the state-of-the-art application of ML-based techniques to improve ZSM performance. To this end, the main related standardization activities and the aligned international projects and research efforts are deeply examined. From this dissection, the skyrocketing growth of the ZSM paradigm can be observed. Concretely, different standardization bodies have already designed reference architectures to set the foundations of novel automatic network management functions and resource orchestration. Aligned with these advances, diverse ML techniques are being currently exploited to build further ZSM developments in different aspects, including multi-tenancy management, traf c monitoring, and architecture coordination, among others. However, different challenges, such as the complexity, scalability, and security of ML mechanisms, are also identi ed, and future research guidelines are provided to accomplish a rm development of the ZSM ecosystem.