While GPS has traditionally been the primary positioning technology, 3GPP has more recently begun to include positioning services as native, built-in features of future-generation cellular networks. With Release 16 of the 3GPP, finalized in 2021, a significant standardization effort has taken place for positioning in 5G networks, especially in terms of physical layer signals, measurements, schemes, and architecture to meet the requirements of a wide range of regulatory, commercial and industrial use cases. However, experimentally driven research aiming to assess the real-world performance of 5G positioning is still lagging behind, root causes being (i) the slow integration of positioning technologies in open-source 5G frameworks, (ii) the complexity in setting up and properly configuring a 5G positioning testbed, and (iii) the cost of a multi-BS deployment. This paper sheds some light on all such aspects. After a brief overview of state of the art in 5G positioning and its support in open-source platforms based on software-defined radios (SDRs), we provide advice on how to set up positioning testbeds, and we demonstrate, via a set of real-world measurements, how to assess aspects such as reference signal configurations, localization algorithms, and network deployments. Our contribution further includes an assessment of the efficacy of utilizing measurements obtained from a single-link limited-size testbed to forecast localization performance in more elaborate (and hence more expensive) multi-node network settings. We posit that our methodological insights can assist in lowering the entry cost barriers associated with conducting 5G positioning experiments and, consequently, promote additional experimental research in this domain.

Accurate localization is an essential functionality of 5G and beyond systems to enable location-based applications, such as autonomous vehicles and emergency response. Nevertheless, the integrity of location data faces challenges not only from unintentional sources of error, such as wireless propagation impairments and synchronization failures but also from malicious and intentional threats, such as spoofing attacks. This paper specifically addresses the risk to localization integrity posed by malicious attacks. It provides a framework for modeling security threats at the physical layer of cellular positioning, with a focus on 5G and beyond systems. Two detection methods are proposed to mitigate the impact of spoofing attacks, by leveraging cross-correlation analysis and Gaussian Mixture Models (GMMs). These methods leverage standard metrics already defined in the localization procedure, thus eliminating the need for additional signal processing steps. Simulation results in 3GPP standard-compliant scenarios demonstrate the effectiveness of these methods in significantly reducing the integrity risk under attack conditions, thus providing a foundation for developing resilient mobile network location-based services.

IoT devices present an ever-growing domain with multiple applicability. This technology has favored and still favors many areas by creating critical infrastructures that are as profitable as possible. This paper presents a hierarchical architecture composed of different licensing entities that manage access to different resources within a network infrastructure. They are conducted on the basis of well-drawn policy rules. At the same time, the security side of these resources is also placed through a context awareness module. Together with this technology, IoT is used and Blockchain is enabled (for network consolidation, as well as the transparency with which to monitor the platform). The ultimate goal is to implement a secure and scalable security platform for the Smart Grid. The paper presents the work undertaken in the SealedGRID project and the steps taken for implementing security policies specifically tailored to the Smart Grid, based on advanced concepts such as Opinion Dynamics and Smart Grid-related Attribute-based Access Control.

The integrity and reliability of the positioning information are crucial for applications, including safety-critical ones, that rely on cellular network users' locations. Release 18 of the Third Generation Partnership Project (3GPP) broadens the notion of integrity, which was initially applied only to positioning based on Global Navigation Satellite System (GNSS), to include positioning based on Radio Access Technology (RAT). This paper presents a general 3GPP-compliant framework to assess 5G positioning integrity. The framework is based on uncertainty quantification and is applied to classical time-difference-of-arrival positioning algorithms and to more advanced positioning methods that rely on machine learning. If an integrity alert is raised, error mitigation techniques are also proposed to enable resilient positioning through the selection of reliable network nodes. Results obtained by using a 3GPP-compliant dataset of position estimates in an indoor open-office scenario demonstrate promising detection and resilience capabilities in the presence of integrity breaches.