Software-Defined Networking (SDN) enables flexible and programmable control over network behavior through the deployment of multiple control applications. However, when these applications operate simultaneously, each pursuing different and potentially conflicting objectives, unexpected interactions may arise, leading to policy violations, performance degradation, or inefficient resource usage. This paper presents a Digital Twin (DT)-based framework for the early detection of such application-level conflicts. The proposed framework is lightweight, modular, and designed to be seamlessly integrated into real SDN controllers. It includes multiple DT models capturing different network aspects, including end-to-end delay, link congestion, reliability, and carbon emissions. A case study in a smart factory scenario demonstrates the framework’s ability to identify conflicts arising from coexisting applications with heterogeneous goals. The solution is validated through both simulation and proof-of-concept implementation tested in an emulated environment using Mininet. The performance evaluation shows that three out of four DT models achieve a precision above 90%, while the minimum recall across all models exceeds 84%. Moreover, the proof of concept confirms that what-if analyses can be executed in a few milliseconds, enabling timely and proactive conflict detection. These results demonstrate that the framework can accurately detect conflicts and deliver feedback fast enough to support timely network adaptation.

UAV-assisted networks provide a rapid and flexible solution for deploying communication infrastructures in environments with limited or no conventional access, such as rural areas or disaster-stricken regions. However, the inherent constraints of UAVs, particularly their limited flight time, necessitate frequent node replacements, which disrupt microservices hosted on the UAVs. This paper introduces a model for live migration of stateful microservices in UAV-assisted networks, leveraging pre-copy mechanisms and containerized environments to minimize service downtime and ensure state preservation. The proposed architecture incorporates a Digital Twin for real-time monitoring, a Swarm Management Function for orchestrating UAV replacements, and a Central Image Server for managing storage layers. By combining these elements, the model achieves seamless handovers, reducing disruptions and enabling reliable deployment of state-sensitive applications. Potential use cases, including real-time data analysis and edge computing, underscore the advantages of this approach. Future work will focus on evaluating performance metrics such as service downtime, network overhead, and energy consumption to further optimize this solution.

The classification of Congestion Control Algorithms (CCAs) is vital in current networks, where increasing traffic demands and dynamic conditions challenge their stability and performance. CCAs play a fundamental role in managing congestion, balancing throughput, minimizing latency, and reducing packet loss to ensure reliable data transmission across diverse scenarios. Despite their importance, accurately identifying the CCA in use remains a challenging task; critical for optimizing resource allocation and enhancing Quality of Service (QoS). This paper presents a framework that leverages P4-programmable switches for real-time extraction of key traffic metrics, including queuing delay, interarrival time, queue depth, RTT, and sending rate. These metrics are analyzed using a Random Forest classifier to predict the CCA in use with high accuracy. Extensive experiments in a controlled network environment, featuring a bottleneck link and flows utilizing CCAs such as Cubic, Reno, BBR, and Vegas, validate the effectiveness of our approach.

This paper presents empirical results from the use of multi-UAV ad hoc networks that also perform edge computing through the deployment and execution of microservices requested by user equipment on the ground. For this purpose, the most important aspects on how the UAV-based mesh network is configured and how user equipment on the ground accesses it are detailed. Four different tests are provided, all conducted with affordable user-grade hardware, explained and analyzed. Results demonstrate how the use of mesh-connected swarms of UAVs is a viable solution for providing connectivity and edge computing capabilities to large areas of terrain where there is no traditional network infrastructure. Given a suitable distance between UAVs, useful throughput values of at least 10 Mbps are observed, reaching up to 25 Mbps in specific situations. In addition to that, it is also observed that the communication channel between UAVs is unstable and that special attention must be paid to the distance between them.

The Internet has become the driving wheel of social interactions as well as economic growth, but its access is vastly unequal. Noticeably, such rural dwellers experience a digital service access gap, due to the inadequate broadband infrastructure available to them when compared with their urban counterparts. This paper is aimed at overcoming the gap between rural communities with no reliable Internet service by deploying the service through the use of Unmanned Aerial Vehicles (UAVs). By decomposing the Internet of Things (IoT) applications into elements (namely microservices) and deploying them through UAVs, latency is shortened, distributing workload among UAVs while limiting microservice deployment to optimize request execution, making the quality service improvement, essential for such applications. Simulations revealed the effectiveness of our approach, as it indicated the lowest latency when a UAV connected to several users in scenarios where there was no Internet connectivity.

Rural areas broadband access suffers from the limited investment of network operators, due to the forecasted return on investment. As a consequence, digital services such as eHealth, remote education, or smart agriculture cannot be offered to the rural population. In this context, research on Unmanned Aerial Vehicles (UAV) networks has emerged, which aims to solve the coverage problem by relying on small cells mounted on UAVs to provide coverage. From the network Quality of Service (QoS) point of view, i.e., the performance offered to users according to certain parameters such as delay, reliability, and throughput provisioning can be identified as one of the weak points. For the problem of maximizing throughput, the main solution is to group several UAVs in the same area. However, as the offered throughput increases, the power consumption will also increase. In this context, this paper proposes a genetic algorithm to solve the problem of jointly maximizing the offered throughput in rural scenarios where users request microservice-based IoT applications while minimizing the energy consumption of the swarm of UAVs. The algorithm is defined and evaluated in realistic scenarios, demonstrating its effectiveness on increasing the throughput while decreasing the number of UAVs that are required.