Tecnologies de la Informació i de Xarxes - Doctorat - UOC R&I

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Tecnologies de la Informació i de Xarxes

Wireless Networks and IoE

Available thesis proposals:

Agentic AI 6G Network Orchestration
Context-aware applications for ambient intelligence
Developing resilient GNSS positioning methods for high-precision, mission-critical applications
Enabling energy-autonomous connectivity for Ambient IoT communication
Next-generation Ambient IoT devices
Sustainable ubiquitous sensing

Thesis proposals	Researchers	Research Group
Sustainable ubiquitous sensing Automated ubiquitous sensing will represent a major change in current societal challenges in terms of efficiency (i.e. automatization), sustainability (better models and decision-making), and social wellbeing (security and improved job market). Automated ubiquitous sensing would, for instance, detect humidity or thin water ponds on a road avoiding possible car accidents in a smart city context, improve recycling processes thanks to automated items classification, or predict dehydration of a person in an ambient assisted living environment. Current solutions for automated sensing, including active and passive sensors, use specific circuitry for sensing besides wireless communications technologies to transmit the measurements. This approach implies battery management complexity and/or expensive customized technology, being inappropriate from the sustainability perspective. This proposal aims to exploit backscatter low-cost communication technologies like Ultra High Frequency (UHF) Radio Frequency Identification (RFID) to act as sustainable ubiquitous sensors. The goal is to improve the integration of passive sensing in the environment, towards a seamless sustainable digitization. For more information see [1,2,3,4]. [1] Floerkemeier, Bhattacharyya and Sarma (2010): https://doi.org/10.1007/978-1-4419-1674-7_21 [2] Melià-Seguí and Vilajosana (2019): https://doi.org/10.1109/RFID.2019.8719092 [3] Lejarreta-Andrés, Melià-Seguí, Bhattacharyya, Vilajosana and Sarma (2022): https://doi.org/10.1109/JSEN.2022.3188936 [4] Melià-Seguí, Bhattacharyya, López-Soriano, Vilajosana and Sarma (2024): https://doi.org/10.1109/JSEN.2023.3339117	Dr Joan Melià Mail: melia@uoc.edu Dr Xavier Vilajosana Mail: xvilajosana@uoc.edu	WINE
Context-aware applications for ambient intelligence Smart cities are a new scenario where a variety of data sources open the door to innovative applications, with the final goal of improving the quality of life of citizens, industry competitiveness and government. The Internet of everything is responsible for collecting and transmitting this data at different levels, by using a wide variety of sensors (including peoples' smartphones). We propose to make use of contextual intelligence tools and techniques (like data mining or statistical learning) to process signals (accelerometers, acoustic, etc.) with the overall goal of transforming the above collected data into useful information, enabling new applications or improving existing ones within the ambient intelligence context.	Dr Joan Melià Mail: melia@uoc.edu Dr Carlos Monzo Mail: cmonzo@uoc.edu	WINE
Agentic AI 6G Network Orchestration The demand for highly flexible and adaptable communications networks, especially cellular systems, has been a core driver over the last decade. This has spurred academic and industry efforts to design architectures that enable fully autonomous management and orchestration, largely by exploiting network data with Generative AI solutions [1, 2]. In parallel, the Intent-Based Networking (IBN) paradigm has gained traction, driven by the inherent complexity of modern networks and the need to manage diverse service Quality of Service (QoS) requirements. IBN harnesses the power of Large Language Models (LLMs) to streamline the configuration of complex networks and improve the comprehension of autonomous network decisions [3, 4, 5]. Moving toward real autonomous network management, Agentic AI native solutions are now emerging as flexible, scalable, and reliable frameworks for the future 6G network infrastructure [6]. Despite the clear research objective, current solutions are in their infancy, underscoring the immediate need for novel contributions. This research proposal directly addresses this gap by contributing to the definition and development of Agentic AI-based network management and orchestration frameworks. Our work will integrate forefront AI and networking technologies, ensuring alignment with key network architectures and close coordination with standardization bodies. References: [1] L. Bariah and M. Debbah, "AI Embodiment Through 6G: Shaping the Future of AGI," in IEEE Wireless Communications, vol. 31, no. 5, pp. 174-181, October 2024, doi: 10.1109/MWC.015.2300521. [2] Y. Xiao, G. Shi and P. Zhang, "Toward Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach," in IEEE Communications Magazine, vol. 63, no. 9, pp. 68-74, September 2025, doi: 10.1109/MCOM.001.2500005. [3] Y. Wang, C. Yang, T. Li, Y. Ouyang, X. Mi and Y. Song, "A Survey on Intent-Driven End-to-End 6G Mobile Communication System," in IEEE Communications Surveys & Tutorials, doi: 10.1109/COMST.2025.3575041. [4] A. Mekrache, A. Ksentini and C. Verikoukis, "Intent-Based Management of Next-Generation Networks: an LLM-Centric Approach," in IEEE Network, vol. 38, no. 5, pp. 29-36, Sept. 2024, doi: 10.1109/MNET.2024.3420120. [5] A. Mekrache, A. Ksentini and C. Verikoukis, "DMO-GPT: An Intent-Driven Framework for Distributed 6G Management and Orchestration," in IEEE Communications Magazine, doi: 10.1109/MCOM.001.2500258. [6] M. Elkael, S. D'Oro, L. Bonati, M. Polese, Y. Lee, K. Furueda and T. Melodia, “AgentRAN: An Agentic AI Architecture for Autonomous Control of Open 6G Networks”, Arxiv.org, Aug. 2025. https://arxiv.org/abs/2508.17778	Dr Xavier Vilajosana Mail: xvilajosana@uoc.edu Dr Ferran Adelantado Mail: ferranadelantado@uoc.edu	WINE
Developing resilient GNSS positioning methods for high-precision, mission-critical applications GNSS underpins safety-critical navigation for UAVs, road vehicles and rail, yet urban canyons, unintentional interference, intentional jamming and spoofing can degrade or corrupt positioning. We propose a research programme that (i) protects the signal chain against denial and deception, and (ii) reconstructs or recovers the navigation state so that accurate positioning is maintained, even under severe interference.	Dr Xavier Vilajosana Mail: xvilajosana@uoc.edu	WINE
Enabling energy-autonomous connectivity for Ambient IoT communication The Ambient IoT paradigm envisions a pervasive network of battery-free or energy-autonomous devices, embedded seamlessly in the environment, capable of sensing, communicating and interacting without explicit human intervention. Despite recent progress, major challenges still remain in realizing robust, large-scale Ambient IoT deployments and ensuring their harmonious coexistence with legacy networks. This project aims to develop solutions for boosting the link budget of passive (and semi-passive) devices to increase the detection range, transmit larger volumes of data and achieve higher robustness. To this end, the project will investigate novel reconfigurable backscatter mechanisms and cooperative relaying schemes to enhance the communication range and reliability under stringent power constraints. It will explore the use of energy from nearby devices, base stations and environmental sources (i.e. solar) to design efficient methods and protocols for energy harvesting. The project is a research collaboration with the Aalborg University (AAU), Denmark. [REF] Perovskite PV-Powered RFID: Enabling Low-Cost Self-Powered IoT Sensors	Dr Joan Melià Mail: melia@uoc.edu Dr Marta Gatnau Sarret Mail: mgatnaus@uoc.edu	WINE
Next-generation Ambient IoT devices The Ambient Internet of Things (AIoT) relies fundamentally on pervasive, zero-power sensing devices (truly battery-free or passive), seamlessly embedded in the environment for autonomous data collection. However, achieving robust, wider-area truly passive AIoT deployments is constrained by critical design trade-offs, particularly antenna design and substrate materials for performance optimization in different environments. This research line aims to develop solutions for boosting the link budget of passive (and semi-passive) devices, focusing on improvement of the end device. We aim to explore different technologies and techniques, including chip-enabled solutions (i.e. reconfigurable backscatter mechanisms) as well as chipless approaches to ensure robustness and improved-range operation under stringent power constraints, enabling the next-generation AIoT devices. This project is included within the EU-funded Pathfinder GAIA project (2026-2028) [1]. [1] https://www.uoc.edu/en/news/2025/gaia-project-for-sustainable-iot	Dr Joan Melià Mail: melia@uoc.edu	WINE