Alocação dinâmica de recursos em fatias de redes IoT não-3GPP envolvendo VANTs

• Ano de defesa: 2025
• Autor(a) principal: Silva, Rogério Sousa e
• Orientador(a): Cardoso, Kleber Vieira
• Banca de defesa: Cardoso, Kleber Vieira , Oliveira Júnior, Antonio Carlos de , Abelém, Antônio Jorge Gomes , Both, Cristiano Bonato , Rocha, Flávio Geraldo Coelho
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação (INF)
• Departamento: Instituto de Informática - INF (RMG)
• País: Brasil
• Palavras-chave em Português: Veículos aéreos não tripulados; Programação linear inteira mista; Aprendizado por reforço profundo; Redes 5G não-3GPP; Fatiamento de redes
• Palavras-chave em Inglês: Unmanned aerial vehicles; Mixed integer linear programming; Deep reinforcement learning; Non-3GPP 5G networks; Non-3GPP network slicing
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.bc.ufg.br/tede/handle/tede/14499

The exponential growth of the Internet of Things (IoT) has introduced increasing challenges to communication infrastructures, particularly in critical scenarios such as natural disasters and densely populated events, where network overload compromises service continuity and quality. In this context, this thesis presents a hybrid approach for dynamic resource allocation in non-3GPP IoT networks, integrating network slicing (NS), heterogeneous access (Multi-RAT), and unmanned aerial vehicles (UAVs) equipped with LoRaWAN gateways. The proposed hybrid approach synergistically combines the precision of exact optimization methods based on Mixed Integer Linear Programming (MILP), employed for determining the optimal initial positioning of UAVs, with the adaptive flexibility of advanced Deep Reinforcement Learning (DRL) algorithms, which enable dynamic and autonomous repositioning in variable environments. The goal of the first stage is to minimize operational and deployment costs while maximizing Quality of Service (QoS), while the second stage is to facilitate the autonomous repositioning of UAVs in response to environmental changes and fluctuations in network demand. We develop and assess four DRL algorithms, e.g., SR-DQN, DA-DDDQN, NSE-A2C, and RG2E-PPO. The proposed solutions were validated through realistic simulations using the ns-3 network simulator, in customized scenarios with non-3GPP connectivity. Results demonstrated significant improvements in QoS, reduced number of deployed UAVs, enhanced decision robustness, and increased spectral efficiency, with notable performance from the NSE-A2C and RG2E-PPO algorithms. The hybrid approach enables the creation of a mobile, scalable, and resilient communication infrastructure capable of autonomously and efficiently addressing the specific requirements of diverse IoT applications, particularly in urban and emergency environments with critical connectivity constraints. This thesis contributes to the state of the art by proposing a replicable, sustainable, and service-oriented hybrid architecture for reliable communication in heterogeneous and dynamic networks based on unlicensed technologies. Potential applications include smart cities, disaster response, and temporary connectivity deployment in degraded or non-existent infrastructure scenarios.