Software-Defined Networking (SDN) is a thriving networking architecture that has gained popularity in recent years, particularly as an enabling technology to foster paradigms like edge computing. SDN separates the control and data planes, which are later on synchronised via a control protocol such as OpenFlow. In-band control is a type of SDN control plane deployment in which the control and data planes share the same physical network. It poses several challenges, such as security vulnerabilities, network congestion, or data loss. Nevertheless, despite these challenges, in-band control also presents significant opportunities, including improved network flexibility and programmability, reduced costs, and increased reliability. Benefiting from the previous advantages, diverse in-band control designs exist in the literature, with the objective of improving the operation of SDN networks. This paper surveys the different approaches that have been proposed so far towards the advance in in-band SDN control, based on four main categories: automatic routing, fast failure recovery, network bootstrapping, and distributed control. Across these categories, detailed summary tables and comparisons are presented, followed by a discussion on current trends a challenges in the field. Our conclusion is that the use of in-band control in SDN networks is expected to drive innovation and growth in the networking industry, but efforts for holistic and full-fledged proposals are still needed.

The future generation of mobile networks envision Artificial Intelligence (AI) and the Internet of Things (IoT) as key enabling technologies that will foster the emergence of sophisticated use cases, with the industrial sector being one to benefit the most. This survey reviews related works in this field, with a particular focus on the specific role of network softwarization. Furthermore, the survey delves into their context and trends, categorizing works into several types and comparing them based on their contribution to the advancement of the state of the art. Since our analysis yields a lack of integrated practical implementations and a potential desynchronization with current standards, we finalize our study with a summary of challenges and future research ideas.

Recently, two technologies have emerged to provide advanced programmability in Software-Defined Networking (SDN) environments, namely P4 and XDP. At the same time, the Internet of Things (IoT) represents a pillar of future 6G networks, which will be also sustained by SDN. In this regard, there is a need to analyze the suitability of P4 and XDP for IoT. In this article, we aim to compare both technologies to help future research efforts in the field. For this purpose, we evaluate both technologies by implementing diverse use cases, assessing their performance and providing a quick qualitative overview. All tests and design scenarios are publicly available in GitHub to guarantee replication and serve as initial steps for researchers that want to initiate in the field. Results illustrate that currently XDP is the best option for constrained IoT devices, showing lower latency times, half the CPU usage, and reduced memory in comparison with P4. However, development of P4 programs is more straightforward and the amount of code lines is more similar regardless of the scenario. Additionally, P4 has a lot of potential in IoT if a special effort is made to improve the most common software target, BMv2.

Smart grids (SGs) are essential for the efficient and distributed management of electrical distribution networks. A key task in SG management is fault detection and subsequently, network reconfiguration to minimize power losses and balance loads. This process should minimize power losses while optimizing distribution by balancing loads across the grid. However, the current literature yields a lack of methods for efficient fault prediction and fast reconfiguration. To achieve this goal, this paper builds on DEN2DE, an adaptable routing and reconfiguration solution potentially applicable to SGs, and investigates its potential extension with AI-based fault prediction using real-world datasets and randomly generated topologies based on the IEEE 123 Node Test Feeder. The study applies models based on Machine Learning (ML) and Deep Learning (DL) techniques, specifically evaluating Random Forest (RF) and Support Vector Machine (SVM) as ML methods, and Artificial Neural Network (ANN) as a DL method, evaluating each for accuracy, precision, and recall. Results indicate that the RF model with Recursive Feature Elimination (RFECV) achieves 94.28% precision and 81.05% recall, surpassing SVM (precision 89.32%, recall 6.95%) and ANN (precision 72.17%, recall 13.49%) in fault detection accuracy and reliability.

In-Band enhanced Hybrid Domain Discovery Protocol (ieHDDP) is a novel integral approach for hybrid Software-Defined Networking (SDN) environments that simultaneously provides a topology discovery service and an autonomous control channel configuration in the band. This contribution is particularly relevant since, to the best of our knowledge, it is the first all-in-one proposal for SDN capable of collecting the entire topology information (type of devices, links, etc.) and establishing in-band control channels at once in hybrid SDN environments (composed by SDN/no-SDN, wired/wireless devices), even with isolated SDN devices. ieHDDP facilitates the integration of heterogeneous networks, for example, in 5G/6G scenarios, and the deployment of SDN devices by using a simple exploration mechanism to gather all the required topological information and learn the necessary routes between the control and data planes at the same time. ieHDDP has been implemented in a well-known SDN software switch and evaluated in a comprehensive set of randomized topologies, acknowledging that ieHDDP is scalable in representative scenarios.

The evolution of Industrial Internet of Things (IIoT) systems demands flexible and intelligent architectures capable of addressing low-latency requirements, real-time analytics, and adaptive resource management. In this context, softwarized edge computing emerges as a key enabler, supporting advanced IoT deployments through programmable infrastructures, distributed intelligence, and seamless integration with cloud environments. This paper presents an extended and publicly available proof of concept (PoC) for a softwarized, data-driven architecture designed to operate across the cloud/edge/IoT continuum. The proposed architecture incorporates containerized microservices, open standards, and ML-based inference services to enable runtime decision-making and on-the-fly network reconfiguration based on real-time telemetry from IIoT nodes. Unlike traditional solutions, our approach leverages a modular control plane capable of triggering dynamic adaptations in the system through RESTful communication with a cloud-hosted inference engine, thus enhancing responsiveness and autonomy. We evaluate the system in representative IIoT scenarios involving multi-agent collaboration, showcasing its ability to process data at the edge, minimize latency, and support real-time decision-making. This work contributes to the ongoing efforts toward building advanced IoT ecosystems by bridging conceptual designs and practical implementations, offering a robust foundation for future research and deployment in intelligent, software-defined industrial environments.