Although the complete scope of the sixth generation of mobile technologies (6G) is still unclear, the prominence of the Internet of Things (IoT) and Artificial Intelligence (AI) / Machine Learning (ML) in the networking field is undeniable. In this regard, key technology enablers for the previous generation, 5G, such as software-defined networking and network function virtualization, fall short to accomplish the stringent requirements envisioned for 6G verticals. This PhD thesis goes back to basics, by exploring missing functionality gaps in relation to these technologies, in order to provide the ''glue'' for holistic and fully-fledged networking solutions for 6G, aligned with standards and industry recommendations. Although ambitious, and in a very early stage, this PhD thesis illustrates an initial design for in-band control in Software-Defined Networking (SDN) that could facilitate the interoperability among constrained IoT devices. The current design demonstrates promising results in terms of resource-usage and robustness, which are pivotal features for constrained networks. Next steps include the integration of the approach with a real testbed comprised of constrained IoT devices and the implementation of a federated learning environment at the edge.

翻译：尽管第六代移动通信技术（6G）的完整图景尚不明确，但物联网（IoT）及人工智能（AI）/机器学习（ML）在网络领域的主导地位已无可争议。在此背景下，软件定义网络与网络功能虚拟化等上一代（5G）关键使能技术，已难以满足6G垂直行业严苛的愿景需求。本博士论文回归根本，通过探究这些技术中缺失的功能性缺口，旨在为6G提供符合标准与行业建议的整体化、完备化网络解决方案的"粘合剂"。尽管该研究雄心勃勃且尚处早期阶段，但本文已在软件定义网络（SDN）带内控制方面完成初步设计，该设计可促进受限物联网设备间的互操作性。现有设计在资源利用率和鲁棒性方面展现出有前景的结果，这两项特征对受限网络至关重要。下一步工作包括：将该方案与包含受限物联网设备的真实测试平台集成，并在边缘侧部署联邦学习环境。