Open Platforms for Connected Vehicles

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Demo: Open source testbed for vehicular communication

The challenge of enabling the communications between the vehicle and its surroundings is being faced by the entire automotive industry, while the main standardization bod- ies are undergoing a huge effort to propose new solutions and improve the existing ones. The lack of open source solu- tions for vehicular communications penalizes the technology advances, and for this reason we present an open source plat- form 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 don- gles, 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

Mobile RF Scenario Design for Massive-Scale Wireless Channel Emulators

Large-scale wireless emulation is gaining momentum nowadays, thanks to its potential in the development and deployment of advanced use cases for next-generation wireless networks. Several novel use cases are indeed emerging, including massive MIMO, millimeter wave beamforming and AI-based Vehicle-to-Everything (V2X) optimized communication. The development and testing of a wireless application, especially at a large scale and when dealing with mobile nodes, faces several challenges that cannot be solved by simulation frameworks alone. Thus, massive-scale channel emulators are emerging, enabling the emulation of realistic scenarios which leverage real hardware and radio signals. However, this is a complex task due to the lack of realistic scenarios based on real datasets. We thus propose a novel framework for the design and generation of channel emulation scenarios starting from real mobility traces, either generated by means of dedicated tools, or collected on the field. Our framework provides a practical way of generating mobility scenarios with vehicles, pedestrians, drones and other mobile entities. We detail all the steps foreseen by our framework, from the provision of the traces and radio parameters, to the generation of a matrix describing the delay and IQ samples for each time instant and node in the scenario. We also showcase the potentiality of our proposal by designing and creating a vehicular 5G scenario with 13 vehicles, starting from a recently-disclosed open dataset. This scenario is then validated on the Colosseum channel emulator, proving how our framework can provide an effective tool for large-scale wireless networking evaluation

A Flexible, Protocol-agnostic Latency Measurement Platform

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

Characterization and performance evaluation of 802.11p NICs

The automotive industry is scrambling to equip high- and middle-segment vehicles with communication capabilities that will enable the commercialization of connected vehicles in the near future. Although both IEEE and 3GPP are devel- oping new solutions, it is likely that IEEE 802.11p will be the protocol of choice. In this paper, we develop an open-source testing framework for IEEE 802.11p cards and character- ize the performance of Unex DHXA-222 cards in terms of throughput and packet loss, especially when different traffic classes, hence access categories, are selected

ONIX: Open Radio Network Information eXchange

While video-on-demand still takes up the lion's share of Internet traffic, we are witnessing a significant increase in the adoption of mobile applications defined by tight bit rate and latency requirements (e.g., augmented/virtual reality). Supporting such applications over a mobile network is very challenging due to the unsteady nature of the network and the long distance between the users and the application back-end, which usually sits in the cloud. To address these and other challenges, like security, reliability, and scalability, a new paradigm termed multi-access edge computing (MEC) has emerged. MEC places computational resources closer to the end users, thus reducing the overall end-to-end latency and the utilization of the network backhaul. However, to adapt to the volatile nature of a mobile network, MEC applications need real-time information about the status of the radio channel. The ETSI-defined radio network information service (RNIS) is in charge of providing MEC applications with up-to-date information about the radio network. In this article, we first discuss three use cases that can benefit from the RNIS (collision avoidance, media streaming, and Industrial Internet of Things). Then we analyze the requirements and challenges underpinning the design of a scalable RNIS platform, and report on a prototype implementation and its evaluation. Finally, we provide a roadmap of future research challenges

A Multi-stack Simulation Framework for Vehicular Applications Testing

The vast majority of vehicular applications leverage vehicle-to-everything communications (V2X) to increase road safety, optimize the available transportation resources, and improve the user experience. Because of the complexity and the high deployment costs of vehicular applications, it is usually convenient to extensively test them by simulation. We present an open-source simulation framework for the ns-3 simulator, featuring state-of-the-art vehicular communication models, in which the mobility is managed by the SUMO (Simulation of Urban MObility) simulator. Unlike other simulation frameworks, where the user is mostly limited to a single communication stack, our framework unifies multiple stacks under a single open-source repository. The framework is designed to make it easier to configure the communication stacks, and to enable a fast and easy deployment of vehicular applications. It comes with the support for centralized and distributed vehicular network architectures, embedding the IEEE 802.11p, 3GPP C-V2X Mode 4 and LTE communication stacks, and with vehicular messages dissemination stacks compliant with ETSI standards. We also present two sample applications thought to show the potentiality of the framework, namely an area speed advisory and an emergency vehicle alert

The role of immune suppression in COVID-19 hospitalization: clinical and epidemiological trends over three years of SARS-CoV-2 epidemic

Specific immune suppression types have been associated with a greater risk of severe COVID-19 disease and death. We analyzed data from patients >17 years that were hospitalized for COVID-19 at the “Fondazione IRCCS Ca′ Granda Ospedale Maggiore Policlinico” in Milan (Lombardy, Northern Italy). The study included 1727 SARS-CoV-2-positive patients (1,131 males, median age of 65 years) hospitalized between February 2020 and November 2022. Of these, 321 (18.6%, CI: 16.8–20.4%) had at least one condition defining immune suppression. Immune suppressed subjects were more likely to have other co-morbidities (80.4% vs. 69.8%, p < 0.001) and be vaccinated (37% vs. 12.7%, p < 0.001). We evaluated the contribution of immune suppression to hospitalization during the various stages of the epidemic and investigated whether immune suppression contributed to severe outcomes and death, also considering the vaccination status of the patients. The proportion of immune suppressed patients among all hospitalizations (initially stable at <20%) started to increase around December 2021, and remained high (30–50%). This change coincided with an increase in the proportions of older patients and patients with co-morbidities and with a decrease in the proportion of patients with severe outcomes. Vaccinated patients showed a lower proportion of severe outcomes; among non-vaccinated patients, severe outcomes were more common in immune suppressed individuals. Immune suppression was a significant predictor of severe outcomes, after adjusting for age, sex, co-morbidities, period of hospitalization, and vaccination status (OR: 1.64; 95% CI: 1.23–2.19), while vaccination was a protective factor (OR: 0.31; 95% IC: 0.20–0.47). However, after November 2021, differences in disease outcomes between vaccinated and non-vaccinated groups (for both immune suppressed and immune competent subjects) disappeared. Since December 2021, the spread of the less virulent Omicron variant and an overall higher level of induced and/or natural immunity likely contributed to the observed shift in hospitalized patient characteristics. Nonetheless, vaccination against SARS-CoV-2, likely in combination with naturally acquired immunity, effectively reduced severe outcomes in both immune competent (73.9% vs. 48.2%, p < 0.001) and immune suppressed (66.4% vs. 35.2%, p < 0.001) patients, confirming previous observations about the value of the vaccine in preventing serious disease

Edge-V: Enabling Vehicular Edge Intelligence in Unlicensed Spectrum Bands

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