Traffic congestion affects both urban and interurban roads, harming economic efficiency and sustainability. Hard Shoulder Running (HSR) has been implemented to enhance highway capacity. However, its effectiveness is constrained by diminished driver awareness resulting from the suboptimal performance of Variable Message Signs (VMSs). This study explores how Cooperative Intelligent Transport Systems (C-ITS) and Vehicle-to-Everything (V2X) technologies can enhance HSR utilization and highway performance. Focusing on the A4 Turin-Venice Italian highway, this work includes experimental measurements on radio propagation and a design study on the Road Side Unit (RSU) deployment. The benefits provided by the use of C-ITS for dynamic lane advisory on the vehicles, is assessed by simulating traffic conditions along the Milan's urban corridor of the A4 highway (one of the most congested in Europe) using a professional microsimulation, calibrated on real traffic data with varying V2X adoption rate at vehicles. Results show that V2X technology significantly improves traffic flow, particularly during peak congestion. The study underscores the importance ofC-ITS in future smart road infrastructure and traffic management.

Federated Learning (FL) has emerged as a pivotal privacy-preserving machine learning paradigm, enabling collaborative model train across distributed data sources. A main issue, however, is the lack of comprehensive testing environments that can accurately emulate real-world conditions at a large scale, particularly the impact of network dynamics and device capabilities on FL algorithm performance. To this end, we introduce FederNet, a novel platform designed to facilitate the development and testing of FL algorithms with realistic network and device emulation. We show how the proposed system provides a versatile platform for researchers to evaluate the performance, robustness, and scalability of FL algorithms under diverse and configurable scenarios. We describe the FederNet architecture, detail its network and device emulation capabilities, and outline potential use cases that demonstrate its utility in advancing FL research. By bridging the gap between algorithmic development and practical deployment challenges, FederNet aims to accelerate the innovation and adoption of FL technologies.

The lack of interoperability between smart home devices prevents the implementation of advanced services and use cases in home and building automation. The latest standardization attempt is the release of the Matter protocol, whose goal is to enable interoperability among devices in a local network. In this paper, we present the integration of the Matter protocol in an interoperable, open-source smart home platform capable of managing heterogeneous Matter networks and exchanging data and commands with a cloud infrastructure, standardizing a Message Queuing Telemetry Transport (MQTT) device-to-cloud interface according to the Matter ontology. We evaluate the integration experience and customization level and measure the latency of different MQTT cloud brokers on cellular and fiber internet connectivity. The qualitative results confirm that Matter core features work properly, even if they are still difficult to configure at this early stage of protocol rollout. The quantitative results show the impact of MQTT broker, internet connectivity, and cloud microservices. Overall, the results demonstrate the potentialities of Matter in becoming the definitive standard for smart home communications and the feasibility of its application outside the local network boundaries, i.e., towards the standard-ization of the device-to-cloud communication.

The widely adopted satellite-based positioning systems have shown limitations in meeting the needs of emerging mobile radio network services, which require consistent, high-quality, real-time positioning data. This study introduces RUN-O-RAN, an innovative network-based ranging system integrated as a micro-service within the 5th generation (5G) Open Radio Access Network (O-RAN). RUN-O-RAN makes opportunistic use of uplink reference signals and is robust against hardware and network impairments. It offers seamless deployment, user transparency, and adaptability to varying application requirements, leveraging the programmability of the network. Through a custom-built testbed based on software-defined 5G base stations (gNBs) and commercial user equipment, we comprehensively evaluate this solution across diverse scenario sets and compare its accuracy against satellite-based positioning. The achieved results demonstrate how this system represents the first effective localization O-RAN micro-service.

Vehicle-to-Everything (V2X) communication enhances road safety and traffic efficiency by supporting Cooperative Intelligent Transport System (C-ITS) services. This study introduces the Vehicle Telemetry Information Message (VTIM), designed to share real-time tyre and road surface condition data from vehicle to roadside unit (RSU). We validate the system in a field test using a connected vehicle equipped with ITS-G5 communication and smart tyre technology. Our analyses evaluate V2X latency in telemetry transmission, along with bitrate and packet error rate at the receiving end. Experimental results confirm the system's ability to facilitate timely and reliable data exchange, enabling vehicle telemetry and road surface monitoring (e.g., water detection) for enhanced road safety services.

Connected and Automated Vehicles (CAVs) are revolutionizing road transport by offering enhanced safety, efficiency, and sustainability. A key requirement for their safe operation on roads is the availability of highly accurate positioning information with ultra-low latency. This study presents the design and assessment of an infrastructure-based vehicle positioning system, focusing on the latency involved in transmitting position information to vehicles using Vehicle-to-Everything (V2X) connectivity. Specifically, we consider a roadside positioning infrastructure that integrates an Ultra Wideband (UWB) technology for positioning and an ITS-G5 V2X connectivity for communication. We measure and analyze the round trip time of the V2X communication link to gain insights on the latency performance. The positioning performance is also analyzed by comparing the trajectory followed by the vehicle with the one planned by the onboard control system. On-field evaluations are conducted in a highway tunnel, demonstrating the ability of the infrastructure localization system in successfully enabling autonomous navigation.

This research presents an Internet of Things (IoT) platform for smart home environments that addresses interoperability and scalability issues among devices of different vendors by standardizing the Message Queuing Telemetry Transport (MQTT) messages and topics. The designed IoT platform is structured according to the 3-layer architecture paradigm, comprising the edge, the fog and the cloud layers. By performing functionality tests to verify the correct readings and control of different smart devices, we validate the interoperability of the full platform. The functionality of the platform is assessed by implementing a fall detection use case, where the field devices are triggered to perform specific actions after receiving an alarm of a person's fall. We analyze the best location for the MQTT broker, quantifying the advantage of a local network deployment, compared to the cloud.

Global air traffic has been steadily growing since the beginning of the new century, increasing the need for accurate and reliable positioning in real-time tracking of multiple aircrafts. This paper presents an Interacting Multiple Model (IMM) tracking solution and an assessment of a real Wide Area Multilateration (WAM) aircraft tracking scenario, where measurements from distributed Ground Stations (GSs) are gathered by a Central Processing Station (CPS) running the tracker. The assessment considers a main European airport, where a network of 44 GSs is used to monitor a congested area of $300 \times 250 \mathrm{~km}$. Tracking measurements refer to time differences of arrival (TDOAs) computed starting from the time of arrival (TOA) measured over downlink signals. Specifically, this work considers messages sent over the aviation transponder interrogation mode S. We present the results on IMM-based WAM tracking on airborne maneuvering targets, showcasing the improvements with respect to the conventional Automatic Dependent Surveillance - Broadcast (ADS-B) solution based on global navigation satellite systems (GNSSs).

In recent years, cooperative positioning technologies have emerged as promising augmentation systems for providing high-accuracy positioning (HAP) in cooperative intelligent transportation systems (C-ITS). Among the approaches, implicit cooperative positioning (ICP) takes advantage of shared target detections between vehicles to create common reference points for localization refinement. Their performance, however, is limited by reliance on predefined parametric models, low scalability and communication overhead. To address these problems, this paper introduces a deep multi-agent reinforcement learning (MARL) framework modelled as a decentralized-partially observable Markov decision process (Dec-POMDP). We propose an ICP-multi-agent proximal policy optimization (MAPPO) algorithm, where distributed agents (i.e., the connected vehicles) learn their dynamics and those of the surrounding targets by performing belief estimation over dynamic cooperation graphs that are continuously adjusted by de/activating communication links with neighbors agents. A C-ITS scenario is simulated in a CARLA environment accounting for realistic vehicle dynamics and inter-vehicle communications. The findings reveal that our ICPMAPPO algorithm, leveraging dynamic decentralized execution and centralized training, outperforms ICP in terms of positioning accuracy and communication efficiency.

This paper focuses on highly precise localization and tracking of vehicles in race circuits, where centimeter-level accuracy is required for safety and for enabling complex maneuvering. Recently, the Annealed Stein Particle Filter (ASPF) has been proposed as a promising Bayesian tracking tool for tracking, showing its superior performances against conventional Bayesian filtering methods, such as the Extended Kalman Filter (EKF) and the Particle Filter (PF). Despite its excellent performances, the ASPF entails large computational complexity, making it unsuitable for highly dynamic vehicular scenarios. To address this shortcoming, we propose a Deep Unfolded ASPF (DU-ASPF), a novel Bayesian tracking algorithm integrating the deep unfolding paradigm where the ASPF operations are rearranged into a sequential structure with learnable weights. Experimental results using raw Ultra-Wide Band (UWB) measurements show that the DU-ASPF is able to substantially speed up the tracking process while maintaining the ASPF accuracy.

This paper considers the problem of cooperative localization of passive objects in a vehicular environment through the fusion of lidar point clouds collected at different moving vehicles and sent to the road infrastructure. Object localization is then used to improve the position estimate of vehicles according to the implicit cooperative positioning paradigm. At first, each vehicle uses a deep neural network (a 3D object detector) to process its lidar point cloud and localize static objects. Then, the set of estimated bounding boxes is sent to the road infrastructure, which performs data association through a message passing neural network to identify the set of measurements originating from the same detected object. Lastly, cooperative localization of objects is backward used to improve vehicle positioning. Simulations of a realistic cooperative lidar sensing scenario with CARLA software highlight improved positioning compared to non-cooperative tracking.

The exchange of sensing information through Vehicle-to-Everything (V2X) communications enables the development of cooperative systems for localization augmentation in connected automated vehicles. In V2X scenarios, the integration of measurements from multiple vehicles enhances the environmental perception which is of the utmost importance for enhanced safety services. In this paper, we propose a Deep Neural Network (DNN)-assisted cooperative localization method that relies on a centralized road infrastructure and a network of lidar sensors at vehicles. The proposed algorithm is referred to as DNN Implicit Cooperative Positioning (DNN-ICP) and performs two tasks. At first, each vehicle processes its lidar point cloud by a 3D object detector to identify static objects in the surrounding. Then, the estimated objects are collected at the road infrastructure which uses the aggregated information to improve the localization. Numerical results in a realistic vehicular scenario are presented to quantify the improvement provided by DNN-ICP with respect to a non-cooperative vehicle positioning scheme, showing the reduction of uncertainty on vehicle positioning.

This paper addresses the problem of indoor positioning, where complex propagation characteristics call for advanced Bayesian filters for accurate position tracking. We propose to employ the Stein Particle Filter (SPF) to approximate the posterior distribution with a set of particles, using the Stein Variational Gradient Descent (SVGD) method. A novel SPF tracking method, referred to as Annealed Stein Particle Filter (A-SPF), is designed by exploiting the annealed scheduling of SVGD. Compared to SPF, the A-SPF captures multi-modal distributions that easily arise in indoor localization problems from multipath without requiring higher number of particles. Experimentation activities are carried out in two indoor scenarios, an office and a machinery area, where Ultra Wide-Band (UWB) technology is used to collect raw data. Results show the improved positioning performance of the proposed A-SPF compared to conventional solutions based on extended Kalman filter and particle filter, as well as with standard SPF.

This paper addresses the problem of multitarget tracking using a network of mobile sensors with unknown positions. In contrast to commonly-used approaches which split the sensor localization and target tracking into two different sub-problems, we propose a holistic approach for joint localization and tracking. The theory of graphical models is used to describe the statistical relationship between sensors, targets, and measurements. To jointly infer the states of sensors and targets, we use the statistical processing of belief propagation.

This paper proposes techniques for Beam Alignment (BA) in millimeter wave (mm-wave) Vehicle-to-Everything (V2X) communications with realistic modeling of the dynamic space-time multipath channels. Starting from existing mm-wave channel models, an extension to simulate consistent dynamics of the multipath parameters as vehicles move is introduced. Different BA techniques are then presented, where side location information is used to assist the selection of the optimal pair of beam-pointers. We claim the possibility to exploit the low-rank (LR) structure of the sparse mm-wave channel matrix, jointly with location-related long-term statistics, to avoid time-consuming scanning of the beamformer codebook. The proposed method uses pre-computed eigen-beamformers based on predicted vehicle location and pre-acquired dataset of geo-referenced long-term channel state information to align the beams. Performance analysis in realistic dynamic channel scenarios indicate that the proposed method outperforms conventional BA strategies, avoiding time-consuming beam sweeping procedures.

In Industry 4.0, real-time location systems are emerging as a key technology to improve the efficiency of industrial processes, as they allow to track any assets or material movement and collect data on their usage. Ultra Wideband (UWB) systems offer unrivaled localization accuracy, but they call for augmentation strategies in environments with complex propagation conditions such as plants or factories with high density of scattering objects and obstructions. In this paper, we focus on Bayesian filtering techniques to counterbalance the detrimental effects induced by non line of sight and dense multipath in a smart factory scenario. We first conduct a set of experimental tests with commercial devices in an industrial facility of Pirelli Tyre S.p.A. located in Milan, Italy. We then use the collected data to design and test augmentation algorithms based on Extended Kalman Filter (EKF) and Particle Filter (PF), fusing Time Difference of Arrival (TDoA) and Angle of Arrival (AoA) signals. Experimental results show that, despite the harsh environment, accurate localization is possible by fusion of hybrid measurements and integration of prior information on the target dynamics and the industrial propagation environment.

This paper focuses on advanced pointing strategies enabling high data-rate directional vehicular communications. New emerging technologies aim to meet the challenging performance requirements of enhanced Vehicle-to-Everything (eV2X) applications by using highly collimated beams, which must rely on a very precise beam alignment. In this work, Free-Space Optics (FSO) is considered, and a system architecture is introduced together with algorithms for an accurate alignment of laser beam. The presented architecture exploits on-board sensor data sharing among vehicles to predict the pointing directions for FSO, thus counteracting the detrimental effect of motion, vibrations and tilting of vehicles. A solution is proposed for an accurate prediction of the FSO pointing direction, based on the sharing of vehicle kinematic data over a parallel wireless control link, augmented by prior information extracted from digital maps of the driving environment. Simulation results point out the challenges of a FSO V2V communication and highlight the feasibility of the proposed solution, considering both state of the art technology and future perspective hardware.

Precise vehicle positioning is a key element for the development of Cooperative Intelligent Transport Systems (C-ITS). In this context, we present a distributed processing technique to augment the performance of conventional Global Navigation Satellite Systems (GNSS) exploiting Vehicle-to-anything (V2X) communication systems. We propose a method, referred to as Implicit Cooperative Positioning with Data Association (ICP-DA), where the connected vehicles detect a set of passive features in the driving environment, solve the association task by pairing them with on-board sensor measurements and cooperatively localize the features to enhance the GNSS accuracy. We adopt a belief propagation algorithm to distribute the processing over the network, and solve both the data association and localization problems locally at vehicles. Numerical results on realistic traffic networks show that the ICP-DA method is able to significantly outperform the conventional GNSS. In particular, the analysis on a real urban road infrastructure highlights the robustness of the proposed method in real-life cases where the interactions among vehicles evolve over space and time according to traffic regulation mechanisms. Performances are investigated both in conventional traffic-light regulated scenarios and self-regulated environments (as representative of future automated driving scenarios) where vehicles autonomously cross the intersections taking gap-availability decisions for avoiding collisions. The analysis shows how the mutual coordination in platoons of vehicles eases the cooperation process and increases the positioning performance.