  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.
