Time Sensitive Networking (TSN) is fundamental for the reliable, low-latency networks that will enable the Industrial Internet of Things (IIoT). Wi-Fi has historically been considered unfit for TSN, as channel contention and collisions prevent deterministic transmission delays. However, this issue can be overcome by using Target Wake Time (TWT), which enables the access point to instruct Wi-Fi stations to wake up and transmit in non-overlapping TWT Service Periods (SPs), and sleep in the remaining time. In this paper, we first formulate the TWT Acceptance and Scheduling Problem (TASP), with the objective to schedule TWT SPs that maximize traffic throughput and energy efficiency while respecting Age of Information (AoI) constraints. Then, due to TASP being NP-hard, we propose the TASP Efficient Resolver (TASPER), a heuristic strategy to find near-optimal solutions efficiently. Using a TWT simulator based on ns-3, we compare TASPER to several baselines, including HSA, a state-of-the-art solution originally designed for WirelessHART networks. We demonstrate that TASPER obtains up to 24.97% lower mean transmission rejection cost and saves up to 14.86% more energy compared to the leading baseline, ShortestFirst, in a challenging, large-scale scenario. Additionally, when compared to HSA, TASPER also reduces the energy consumption by 34% and reduces the mean rejection cost by 26%. Furthermore, we validate TASPER on our IIoT testbed, which comprises 10 commercial TWT-compatible stations, observing that our solution admits more transmissions than the best baseline strategy, without violating any AoI deadline.

This paper presents VaN3Twin—the first open source, full-stack Network Digital Twin (NDT) framework for simulating the coexistence of multiple Vehicle-to-Everything (V2X) communication technologies with accurate physical-layer modeling via ray tracing. VaN3Twin extends the ms-van3t simulator by integrating Sionna Ray Tracer (RT) in the loop, enabling high-fidelity representation of wireless propagation, including diverse Line of Sight (LoS) conditions with a focus on LoS blockage due to other vehicles’ meshes, Doppler effect, and site-dependent effects—e.g., scattering and diffraction. Unlike conventional simulation tools, the proposed framework supports realistic coexistence analysis across Dedicated Short Range Communication (DSRC) and Cellular-V2X (C-V2X) technologies operating over a shared spectrum. A dedicated interference tracking module captures cross-technology interference at the time-frequency resource block level and enhances Signal to Interference plus Noise Ratio (SINR) estimation by eliminating artifacts such as the bimodal behavior induced by separate LoS/NLoS propagation models. Compared to field measurements, VaN3Twin reduces application-layer disagreement by 50% in rural and over 70% in urban environments with respect to current state of the art simulation tools, demonstrating its value for scalable and accurate digital twin–based V2X coexistence simulation.

The deployment of cooperative-intelligent transport systems (C-ITS) has started, and standardization and research activities are moving forward to improve road safety and vehicular efficiency. An aspect that is still felt as a limitation by the research groups active in the field, is the difficulty to validate the solutions with real hardware and software, because of the huge investments that are needed when multiple equipped vehicles need to be considered. In this work, we present a platform with low-cost hardware based on a Raspberry Pi and a Wi-Fi module transmitting at 5.9 GHz, and on the open-source software Open Stack for Car (OScar), which is compliant with the ETSI C-ITS standards. With a limited cost in the order of 200 €, the platform realizes a device which is standard compliant and can be used as either on-board unit (OBU) or road side unit (RSU). The limited cost makes the testbed scalable to several units with limited budget and the limited size makes it also deployable on mini-cars to test advanced connected and autonomous vehicle (CAV) networks and applications. Our tests demonstrate its interoperability with other devices, compliance in terms of power spectrum, and a range of a few hundred meters in line-of-sight (LOS) conditions using the standard settings of ITS-G5.

Connectivity is a key enabler for next-generation autonomous vehicles. Developing and validating Connected and Autonomous Vehicle (CAV) services often requires extensive field testing, especially after initial pre-deployment tests. However, multiple field tests introduce high costs and logistical challenges, with no guarantee of consistent environmental conditions.This paper presents TRACEN-X (Telemetry Replay and Analysis of CAN bus and External Navigation data), the first open framework for collecting and reproducing navigation, sensor, and Controller Area Network (CAN) bus data from real-world scenarios in a controlled lab environment. By recreating real-world conditions, TRACEN-X streamlines development and validation, reducing costs and complexity. We validated our framework using data from a connected vehicle with ADAS sensors and a V2X-equipped stroller, demonstrating its potential in combination with a vehicular network simulator and an open ETSI C-ITS stack implementation.

Time Sensitive Networking (TSN) is fundamental for the low-latency, reliable, and energy-efficient networks that will enable the Industrial Internet of Things (IIoT). Wi-Fi has historically been considered unfit for TSN, as channel contention and collisions prevent deterministic transmission delays. However, this issue can be overcome using Target Wake Time (TWT) to instruct Wi-Fi stations to wake up and transmit in non-overlapped TWT Service Periods (SPs) and sleep in the remaining time. In this paper, we first formulate the TWT Acceptance and Scheduling Problem (TASP), whose objective is to schedule TWT SPs as to maximize traffic throughput and energy efficiency while respecting Age of Information (AoI) constraints. Then, since the TASP is NP-hard, we propose the TASP Efficient Resolver (TASPER), a heuristic strategy to find near-optimal solutions efficiently. Finally, we compare TASPER with several baselines through numerical analysis and simulations, which we performed using a TWT-compatible simulator based on ns-3. We demonstrate that TASPER schedules traffic with up to 21.23% higher priority-weighted admission ratio and saves up to 7.42% energy compared to the ShortestFirst strategy, all while satisfying AoI constraints for 99.5% of transmissions.

The automotive field is evolving towards high levels of automation, requiring seamless data exchange between vehicles through Vehicle-to-Everything (V2X) communications. Direct V2X technology is already being deployed on commercial vehicles, and it has the potential to deliver a range of safety and efficiency benefits on the road. However, the deployment of V2X-based applications is a complex process that demands extensive testing before these systems can be widely used by the public; indeed, high costs and safety concerns are among the main hurdles to overcome before applications leveraging V2X communication can become a reality. It is thus critical to reliably validate through simulation and emulation both the V2X technologies and the applications in realistic scenarios, before performing large-scale road tests. To address this pressing need, we present an open source framework for the virtual validation of V2X-based applications, amenable to the development and testing not only of different access technologies within the same environment (IEEE 802.11p, LTE-V2X, 5G NR-V2X, and LTE), but also of any kind of V2X-based application using ETSI-compliant messages. Our framework, called ms-van3t, is based on the ns-3 and SUMO (Simulation of Urban MObility) simulators, it implements a full ETSI C-ITS stack for CAM, DENM and IVIM messages, and it provides several novel features not found elsewhere. Further, ms-van3t enables the testing of V2X-based applications in HIL (Hardware-In-the-Loop) scenarios, thanks to a dedicated emulation mode, and it allows users to easily select different physical and MAC layer models, seamlessly collecting performance statistics. To showcase the capabilities of the framework, we present three sample applications as well as the performance results we obtained in terms of both application-related and network-related key performance indicators.

Cutting-edge advances in wireless networking will soon enable a new generation of safer, smarter, and more autonomous vehicles. These vehicles will rely on real-time execution of complex Deep Learning (DL) tasks as well as high-speed multimedia streaming between road users for navigation purposes. Relying entirely on cellular networks (i) puts an unnecessary burden on an already overcrowded and expensive licensed spectrum; (ii) increases the latency of edge-offloaded tasks to intolerable levels for vehicular applications. Alongside the usage of a proper network infrastructure, vehicles will need to support on-board and offloaded cooperative intelligence. On this basis, we propose Edge-V , the first framework enabling practical vehicular edge intelligence and high-speed vehicular connectivity, using only unlicensed spectrum bands. Through a DSRC link, Edge-V acquires real-time localized knowledge, and coordinates the use of point-to-point millimeter Wave (mmWave) technologies to deliver high-bandwidth connectivity between vehicles. Edge-V also foresees smart offloading if on-board computing resources are insufficient. We prototype and evaluate Edge-V in a real-world laboratory testbed, showing its advantages with respect to cellular and cloud-based approaches.

Centralized services with Vehicle-to-Infrastructure communication can be leveraged to provide precise and up-to-date maneuver recommendations to autonomous vehicles, enabling higher levels of automation. As centralized services have a complete view of the situation on the road, they can be exploited to enable L4 automation & beyond, as opposed to having each node manage an automated maneuver in a distributed way. It is thus pivotal to develop and deploy efficient real-time services, at the edge of the network, to enable low-latency maneuver management services. This paper presents an innovative open-source, 5G & Multi-Access Edge Computing enabled service, called Server Local Dynamic Map (S-LDM), aimed at collecting information about vehicles, based on standard-compliant messages, to create a map of the road, which can then be efficiently shared with other services managing L4 automation. We validated our service on both simulation and pilot trial on a production 5G network involving three main European Mobile Network Operators and real vehicles from Stellantis.

The challenge of enabling the communications between the vehicle and its surroundings is being faced by the entire automotive industry, while the main standardization bodies are undergoing a huge effort to propose new solutions and improve the existing ones. The lack of open source solutions for vehicular communications penalizes the technology advances, and for this reason we present an open source platform based on PC Engines' boards and Unex's WNICs for the testing of V2X (vehicle-to-everything) applications. Our platform enables the connectivity over a 802.11p channel between two boards that can be deployed as wireless dongles, so it can be used to extend the network capabilities of any kind of computing system. The testbed has been setup to work with several applications: from video streaming, to online gaming, to a containerized version of a latency tester, called LaTe.

Latency is one of the key parameters of any networked system, from vehicular networks to real time video streaming. Being capable of measuring such a parameter can be very important in assessing the performances of devices under test. In this paper, we discuss how we designed a lightweight, flexible, custom latency measurement protocol, LaMP, completely agnostic of lower-layer protocols. We also present the first open source tool leveraging LaMP, called LaTe, running on any Linux-based device, which has been validated through several tests, both involving general purpose laptops and embedded devices for vehicular communications, for which the most important results are presented.

The automotive industry is more and more looking into solutions for connected vehicles, enabling use cases which would be otherwise not realizable. New solutions and protocols are currently being developed both by IEEE and 3GPP; due to its characteristics, IEEE 802.11p will probably be the protocol of choice for all the short range communications between vehicles. As, at the best of our knowledge, very few open source solutions exist, we present an open source platform, based on PC Engines embedded boards and Unex DHXA-222 cards, for the testing and characterization of the vehicular amendment of 802.11.