Digital twins have recently emerged as a trans-formative paradigm in wireless networking, offering promising avenues to enhance network efficiency and adaptability. By digitally replicating the physical network, they enable advanced monitoring, analysis, and optimization—key enablers for next-generation wireless systems. Although extensive efforts have been made in modeling and simulating wireless networks, their accuracy often falls short in practical environments. Real-World conditions, such as physical obstructions and complex radio propagation phenomena, are particularly difficult to replicate without resource-intensive local measurement campaigns. To address this challenge, we propose a lightweight data-driven approach to modeling wireless networks. Specifically, we utilize a simple yet informative Key Performance Indicator—the Packet Delivery Ratio (PDR)—to train link-specific quality prediction models. By training the models individually for each link, we effectively capture network heterogeneity and achieve high prediction accuracy. Moreover, we introduce a dynamic and adaptive approach for continuously selecting the most relevant model for the prediction. Experimental results from a real-world deployment show that this approach achieves a high prediction accuracy over both short and mid-term horizons, highlighting the effectiveness of individualized, adaptive modeling in dynamic wireless environments.

As 5G networks proliferate globally, the need for accurate, reliable, and scalable positioning solutions has become increasingly critical across industries, such as Internet of Things (IoT), healthcare, and autonomous systems. This article comprehensively reviews current and emerging positioning techniques within 5G, exploring the advancements enabled by sidelink communication, reconfigurable intelligent surfaces (RISs), machine learning, and massive multiple-input–multiple-output. We examine the evolution of 5G positioning as defined by key 3GPP releases, and provide a comparative analysis of the techniques in terms of accuracy, cost, and robustness. The review also highlights key challenges, including non-line-of-sight (NLOS) environments, real-time data processing, and security concerns, which must be addressed for widespread adoption. Finally, we discuss future directions for 5G-Advanced and 6G positioning technologies, offering insights into potential improvements and the ongoing evolution of the field.

The Internet of Things (IoT) is transforming all economic sectors by connecting physical assets to the virtual world. The range of low-power connectivity options is continuously widening the range of possible IoT applications. However, too many possibilities often make it hard for industrial specialists to choose the right technology and configuration settings, yet these are crucial decisions. To deeply analyze and compare the performance and the scalability of various solution designs, one proven method is simulation. In this article, we show how IoT network simulation can help to future-proof an IoT connectivity design, without the burden of installing a lot of hardware and writing complicated scripts. Then, as the network simulation process is too complex for most IoT teams, we propose a no-code online IoT network simulation platform to make this powerful tool accessible to all. In particular, we explain how we hide the simulation workflow complexity via relevant abstractions and transform it into intuitive interactions. We illustrate the method and the usage of this promising approach, which can be integrated in a network digital twin. We show how it permits to easily evaluate what-if scenarios in order to answer a set of questions that may arise all along the life cycle of a smart connected solution.

The Internet of Things (IoT) is the convergence of the physical and the digital worlds. It enables a large spectrum of applications such as smart building, smart tracking, smart metering, predictive maintenance, remote control, augmented reality or video surveillance. The diversity of these applications has caused a profusion of the IoT communication technologies offerings for exchanging data between IoT devices and applications. The latter technologies come with different features in terms of range, throughput, latency, scalability, energy, etc. Each technology can fit several use cases and a use case can leverage several technologies. It is complex, yet critical, for an IoT architect to evaluate the adequacy and the limits of a network technology for a targeted application and to continuously optimize its configuration as the deployment evolves. This paper introduces ADIperf, a framework to simplify and systematize the evaluation of the performance of an IoT communication technology for a given IoT use case and context. The ADIperf approach pays special attention to the energy efficiency as well as to the ability of an IoT communication technology to properly scale up with the number of end-devices, with the ultimate goal of giving guidelines and tools for IoT architects to select the technology and configure the network that fulfill their application's needs over time.

Unmanned Aerial Vehicle (UAV) applications and services have gained a huge deployment and adoption in different fields, such as the military domain (Defense or reconnaissance) and the civilian domain (Healthcare, surveillance, and transport). UAV operations are generally critical and require, during operations, a control link with the drones, which should be reliable with very low latency. To ensure low-latency, 5G architecture intends to deploy Mobile Edge Computing (MEC) servers, which provide cloud computing capabilities close to the end-users. Consequently, it is envisioned that the AutoPilot application will be deployed at the MEC in order to ensure a low latency connection to the drones. However, the high mobility of drones makes the migration of the AutoPilot applications among MEC servers unavoidable; in order to maintain a low latency connection with the flying drones. This may lead to frequent downtime of the service, which may impact the AutoPilot performances, and hence service migrations should be limited as much as possible. Accordingly, this paper aims to reduce the number of service migrations of drones by introducing novel algorithms that act at the mission planning phase, where the path of the drones is defined.