The EU-funded Horizon 2020 5GMED project aims to promote Cooperative Intelligent Transportation Systems (CITS) in Europe by deploying Cooperative, Connected, and Automated Mobility (CCAM) services across international borders. In this context, this paper presents a 5G-based road traffic safety & management service based on the development of multiple actors and components capable of assessing the traffic status and providing the proper information to Connected Vehicles (CVs) through 5G and Vehicle-to-Everything (V2X) communication technologies. Performance evaluation has been carried out through real-world experiments and trials conducted along the 5GMED cross-border corridor (CBC) between Spain and France, leading to interesting findings and lessons learned presented in the conclusion of this paper.

Development of maneuver cooperation strategies is essential for enhancing traffic safety and efficiency. In a Vehicle-To-Everything (V2X) ecosystem, Maneuver Coordination Messages (MCM) allow road users to coordinate trajectory intentions based on decision-making mechanisms so they can effectively solve complex interactions such as unsignalized intersection crossings or lane changes. As these mechanisms are still in the early stages of development, research continues exploring their effectiveness and feasibility. In this paper, a Decentralized Maneuver Coordination Service (D-MCS) implementation is evaluated in real-world conditions where Vulnerable Road Users (VRUs) and emergency vehicles are taken into account, assessing the efficiency of the cooperative algorithm by demonstrating a reduction in execution time to complete the maneuvers. The results of the experiments also demonstrate that the proposed DMCS achieves a significant reduction in maneuver coordination time compared to a centralized MCS and improves traffic flow and VRU safety by allowing vehicles to proactively adjust their maneuvers.

One of the key concerns related to the pervasive integration of artificial intelligence and machine learning (AI/ML) models in vehicular-to-everything (V2X) communication systems pertains to adversarial attacks, which may lead trained models to exhibit undesirable behaviors. As security and user safety are tightly coupled in V2X, ensuring the resilience of AI/ML models against adversaries becomes indispensable. However, addressing adversarial attacks poses a challenging task, requiring appropriate countermeasures to elevate the trustworthiness of the targeted AI/ML models. In this paper, we propose a deep reinforcement learning (DRL)-based approach to defend against two data poisoning attacks, namely label-flipping and policy induction. Extensive evaluation with the aid of an open-source dataset demonstrates that our scheme outperforms benchmark classifiers, achieving significantly superior detection performance in the presence of label-flipping attacks. The effectiveness of our DRL-based approach is also showcased under different adversarial strategies in the policy induction attack.

Providing 5G seamless and secure services for train passengers is very challenging. In addition to the challenges related to high-speed, many others, such as low signal quality due to 5G coverage holes, tunnels, and complex orography, may compromise the Quality of Service (QoS) provided to train passengers, especially in trains crossing international borders. To overcome these challenges, the EU-funded Horizon 2020 5GMED project has devised a cross-border 5G network architecture that allows train passengers to have the same QoS they have at home in whichever country they are. The network architecture is based on the concept of train neutral host, which operates as a service provider for other mobile network operators. The communication between the train and the ground is facilitated by a heterogeneous network infrastructure comprised of 5G StandAlone (SA), IEEE 802.11ad, and satellite networks to minimize the probability of having low quality signals along the rail track. The seamless switching between these network technologies is performed using Adaptive Communication System-Gateways (ACS-GW) developed in the project. In this paper, we propose the train neutral host architecture, and present the performance evaluation results obtained in a realistic environment over 5G SA networks deployed on the Mediterranean cross-border corridor between Spain and France. Experimental results show satisfying results in terms of service throughput, latency and train inter-cell handover.

To facilitate the widespread adoption of Cooperative Intelligent Transport Systems (C-ITS) and applications, deploying infrastructure alongside roadways to support vehicle communication becomes imperative. This infrastructure, leveraging Multi-access Edge Computing (MEC), necessitates communication infrastructure integrating two technologies: cellular networks and C-V2X (Cellular Vehicle-to-Everything), thus embracing a Vehicle-to-Infrastructure-to-Vehicle (V2I2V) approach. This paper presents an analysis of various Road-Side Units (RSUs) devices from different manufacturers to be used in these architectures, focusing on two key aspects. Firstly, their user-friendliness to enable programming for seamless integration with the communication protocol stack executed in the MEC. Secondly, their ability to meet the transmission capacity demands in high-density scenarios.

Cooperative, Connected and Automated Mobility (CCAM) and Future Railway Mobile Communications Systems (FRMCS) services usually require uninterrupted seamless connectivity. However, in cross-border scenarios, legacy roaming techniques lead to interruption times in the range of one to two minutes, which results unsuitable for the provisioning of demanding CAM and FRMCS services. This paper presents the implementation of state-of-the-art roaming optimization techniques in both the 5G Core and radio access network, including a novel radio optimization handover mechanism for home-routed roaming (HRR) that enables the completion of roaming procedures in 5G Standalone (SA) networks with short interruption times. In addition, a network key performance indicator (KPI) tool is presented to measure the network performance in terms of latency, throughput, and interruption time during roaming. Unlike previous works, static and dynamic performance evaluations are performed in a realistic environment over 5G SA networks deployed on the Mediterranean cross-border corridor between Spain and France. The proposed optimization mechanism yields average interruption time measurements in the range between 135ms and 155ms, enabling seamless service continuity in crossborder scenarios.

The communication services of future trains require hyperconnectivity between trains and track. Future Railway Mobile Communication System (FRMCS) reserved bands will not be able to cover all digital train requirements including passengers’ services. The 5GMED project adopts the idea where Infrastructure Managers (IMs) will collaborate with Railway Undertaking (RU) to facilitate the connectivity to Mobile Network Operator (MNO) communication services and vice versa (complemented with the use of satellite networks), with an architecture open to build new business models and relationships between stakeholders. This approach encompasses an adaptative Gigabit train-to-track connectivity solution able to fit Digital Train requirements. The 5GMED project will demonstrate these capabilities through four representative services deployed on a TGV of SNCF (French Train Operator) in a challenging scenario: a cross-border section of the Mediterranean corridor between Figueres (Spain) and Perpignan (France).

Emerging vehicle-to-everything (V2X) systems call for a diverse set of novel mechanisms to address vulnerabilities and security breaches. In this context, misbehavior detection approaches aim to detect malicious behavior of rogue V2X entities and possible attacks that may originate from them. In this paper, we introduce a data-driven ensemble framework which jointly leverages clustering and reinforcement learning to detect misbehaviors in unlabeled vehicular data. A rigorous detection assessment using an open-source dataset reveals meaningful performance trends for various attacks. In particular, while the majority of attacks can be effectively detected, detection may be curtailed for certain misbehavior types due to partly inaccurate clustering and erratic activity of the attacker over time. Performance comparison against benchmark detectors reveals the robustness of our approach in the presence of potentially inconsistent or mislabeled training data. The real-time detection capabilities of our framework are also explored in an effort to evaluate its practical feasibility in mission-critical V2X scenarios.

Vehicle-to-everything (V2X) communication is contributing towards the realization of futuristic vehicular networks such as Internet-of-Vehicles (IoV). The IoV is expected to usher in a new direction of intelligence and networking to achieve the goal of intelligent transport systems, which rely on the secure exchange of messages between vehicles and infrastructure. However, the transmission of false/incorrect data by malicious vehicles may cause serious damages on road safety. Therefore, it is crucial to detect safety-threatening incorrect information and mitigate potentially detrimental effects on road users. In this paper, we propose a reinforcement learning (RL)-based misbehavior detection approach for V2X scenarios. In our method, the RL-based detection model processes V2X data broadcast by vehicles as time-series at the roadside units, and classifies incoming data as misbehaving or genuine. We evaluate the proposed RL-based approach for detection of various attack types using an open-source dataset, and compare its performance against recent work in misbehavior detection. Our scheme is able to detect all types of misbehavior with a superior recall of 0.9970 and an F1 score of 0.9845, yielding a significant improvement over the benchmarks. Our research outcomes further reveal that misbehaving vehicles can be detected with a great accuracy of 0.9882 by exploiting real-time V2X information.

The transformative Internet-of-Vehicles (IoV) paradigm comes inadvertently with challenges which involve security vulnerabilities and privacy breaches. In this context, denial-of-service (DoS) attacks may perniciously affect the normal operation of IoV systems by causing extensive periods of network unavailability where legitimate vehicles are prevented from accessing vehicular services. In this paper, we offer an in-depth vulnerability assessment of 5G-enabled IoV systems when DoS attack variants are launched at multiple network domains. We further evaluate the resilience of an IoV-tailored authentication mechanism against DoS attacks under various configurations. A data-driven detection scheme is also proposed to address DoS variants in the radio access network, which take the form of false data injection attacks on the exchanged vehicular information. Our performance assessment with the aid of an open-source dataset reveals that the proposed scheme is able to accurately detect DoS traffic originated from malicious vehicles.

In order to improve road safety, modern vehicles are equipped with smart sensors and Vehicle-to-Everything (V2X) communication technologies that facilitate the exchange of data (e.g., location, speed, road hazards) with other vehicles, the road infrastructure, and pedestrians, thus extending the range of perception beyond the capabilities of on-board sensors. All these data can be processed by a Collision Avoidance service deployed in a mobile edge computing (MEC) platform to guarantee low latency in the detection and localization of road hazards. In this paper, we propose a Collision Avoidance service based on Vanetza, an open-source ETSI ITS protocol stack, and demonstrate its operation using already developed experimental On-Board Units (OBUs) that communicate with the Collision Avoidance service over UDP and MQTT to exchange ETSI ITS messages encoded in ASN.1 and JSON format, respectively. We measure the application-level latency and we observe the benefit of our proposed approach in terms of latency reduction by a factor of 12 with respect to the literature.

The fifth generation (5G) of communication networks aims to accelerate the adoption of incipient vertical industries which will leverage innovative smart applications and services such as Cooperative, Connected and Automated Mobility. One of the objectives globally within that area is reducing to zero the number of fatal vehicle accidents. Unfortunately, human errors are the main cause of them, where vulnerable road users (VRUs) are involved in half of the cases. A possible approach to reduce accidents is estimating the probability of collision between two vehicles based on their estimated trajectories. These trajectories are usually tracked on-board using sophisticated devices such as cameras and LiDAR. However, VRUs are generally not equipped with such equipment and, ideally, VRUs carry smartphones with active geolocation capabilities based on satellite-based positioning systems. In this paper, we propose a novel vehicular service based on a regression algorithm to predict trajectories by uniquely using Cartesian coordinates. We compare different types of regression techniques in terms of prediction time window, position accuracy and processing time using Weka. Results show that the Alternating Model Tree (AMT) technique can predict the next position with an error of less than 3.2 centimeters, increasing up to 1 meter when predicting the next 5 positions with a period of 1 second between consecutive positions. In this case, a prediction time window of 5 s is processed within 1.25 milliseconds. AMT resulted as the lowest complex and most accurate algorithm in a multiple-step prediction position.

Selection of the most appropriate communications technology for a smart grid (SG) application is far from trivial. We propose such a feasibility assessment starting from identification of key performance indicators (KPIs) required for peer-to-peer (P2P) energy trading and grid control operations from a communications perspective. A set of cross-disciplinary KPIs, both quantitative and qualitative, are considered from communications, power, business, actor involvement, financial, and demand side management categories. They serve as a general baseline for use cases, as there have been few previous works attempting to capture the essential features of P2P SG operations. The KPIs are briefly identified along with their relations to P2P energy trading and grid control. A straightforward comparison of the quantitative and qualitative KPIs' impact on technology selection is not feasible. This paper addresses the comparison with: 1) a prioritization of the KPIs using the analytic hierarchy process; 2) a comparison of technology solutions evaluated in our previous works against the KPIs' requirements; and 3) a total feasibility evaluation of the solutions against selected KPIs. The prioritization shows latency, reliability, security, scalability, robustness, costs of information and communication technologies (ICT) devices, and costs of ICT deployment are the most important KPIs in enabling P2P energy trading and grid control. Further, the technology feasibility assessment enables identification of the most suitable candidates for an SG application.

The Energy Harvesting-aware Distributed Queuing access protocol (EH-DQ) is presented in this paper as a novel Medium Access Control protocol for wireless Machineto-Machine networks with energy harvesting capabilities. EHDQ is theoretically modeled to analyze its performance. A performance comparison with a Time Division Multiple Access (TDMA) and an EH-aware Reservation Dynamic Frame SlottedALOHA (EH-RDFSA) shows its superior performance. While TDMA requires updated network information to maintain a collision-free schedule and EH-RDFSA requires to estimate the number of contenders per frame to dynamically adjust the frame length, EH-DQ uses short and fixed frame lengths and does not require knowledge of the network topology or the number of end-devices in advance.

The Energy Harvesting-aware Contention Treebased Access (EH-CTA) protocol is presented in this paper as a novel Medium Access Control (MAC) protocol for wireless Machine-to-Machine (M2M) networks where end-devices are equipped with energy harvesters. The protocol is theoretically modeled to analyze its performance. A performance comparison with an EH-aware Dynamic Frame Slotted-ALOHA (EH-DFSA) shows its superior performance. While EH-DFSA requires an estimation of the number of contenders per frame in order to dynamically adjust the frame length, EH-CTA uses short and fixed frame lengths. This ensures scalability and facilitates synchronization in highly dense M2M networks.

Machine-to-Machine (M2M) networks allow enddevices to communicate without human intervention. Due to the high density of M2M networks, efficient Medium Access Control (MAC) protocols are required to manage the access to the channel. In this paper, we consider an M2M area network composed of hundreds of end-devices that periodically transmit data to a gateway. We evaluate the performance of two Medium Access Control (MAC) protocols based on Frame Slotted-ALOHA (FSA) with Successive Interference Cancellation(SIC): Intra-frame SIC-FSA and Inter-frame SIC-FSA. By means of computer-based simulations, we have compared the delay and energy performance with respect to conventional FSA. Results show that the average delay can be reduced in a 78% and 94% by using Intra-frame SIC-FSA and Inter-frame SIC-FSA, respectively, and the average energy consumed per end-device is reduced in 18% by using Intra-frame SIC-FSA, while Interframe SIC-FSA decreases the energy consumed per end-device in a 22%.

This paper describes an experimental implementation of a variation of the Reverse Direction (RD) Medium Access Control (MAC) Protocol (RDP) defined in the IEEE 802.11n using the Wireless Open-Access Research Platform (WARP). The proposed approach, named Bidirectional MAC (BidMAC), allows the receiver of a valid data sequence to perform an RD transmission to the transmitter without contending for the channel. Whereas in RDP the RD transmission must be initiated by the transmitter, in BidMAC it can be dynamically initiated by the receiver according to its traffic requirements. Previous results based on mathematical analyses and computer-based simulations have shown that BidMAC can better balance downlink and uplink transmission opportunities in a Wireless Local Area Network (WLAN) where the Access Point (AP) handles bidirectional data flows for some of its wireless stations (STAs). This paper aims at going one step further and demonstrating that such superior performance can be attained in real environments. Towards this end, an implementation of BidMAC has been carried out in a reference design of WARP compatible with the IEEE 802.11a/g and tested in a proof-of-concept network formed by an AP and two STAs. Experimental results confirm the superior performance of BidMAC when compared to the legacy Distributed Coordination Function (DCF) of the IEEE 802.11 versus the traffic load, packet length, and data rate, yielding gains of up to 60%.*

We consider a wireless Machine-to-Machine (M2M) area network where a gateway periodically collects data from a group of end-devices equipped with energy harvesters. While the use of energy harvesters ideally provides infinite lifetime, the unpredictable amount of harvested energy may not guarantee that all data transmissions can be done in due time because of temporary energy shortages. We propose in this paper the Energy Harvesting-aware Reservation Dynamic Frame Slotted-ALOHA (EH-RDFSA) protocol as a solution suitable for managing the access of end-devices that transmit bursts of data packets while taking into account the energy availability. We derive a model based on a discrete-time Markov chain to analyze the evolution of the energy available in an end-device and to evaluate the performance of the network. In particular, we compute the data delivery ratio, which measures the ability of the protocol to successfully transmit data to the gateway without depleting the energy reserves of the end-devices, and the time efficiency, which measures the amount of data that can be transmitted in a given period of time. We have validated the accuracy of the analysis by means of computer-based simulations. Results show that the overall performance is influenced by the energy harvesting rate and the amount of data to transmit from each end-device. Finally, we have compared the performance of EH-RDFSA with that of DFSA and Time Division Multiple Access (TDMA) protocols.

Machine-to-Machine (M2M) area networks must provide connectivity between an M2M gateway and a large number of energy-constrained M2M devices. Attaining high energy efficiency is essential in order to prolong devices lifetime. In this paper, we consider a wireless M2M area network composed of hundreds or even thousands of dormant devices that wake up periodically to transmit data upon request from a gateway. We theoretically analyze the energy efficiency of a Medium Access Control (MAC) protocol that uses a tree-splitting algorithm to resolve the collisions among devices: the Distributed Queuing (DQ) access. Computer-based simulations have been carried out to validate the accuracy of the analytical model and to evaluate and compare the energy consumption of devices using also a basic Contention Tree Algorithm (CTA) and Frame Slotted-ALOHA (FSA). Results show that DQ can reduce energy consumption in more than 35% with respect to CTA and in more than 80% with respect to FSA in dense M2M area networks with devices in compliance with the IEEE 802.15.4 physical layer.

Attaining very high energy efficiency is one of the big challenges to implement Machine-to-Machine (M2M) networks whose lifetime (without human intervention) must be measured in years. In this paper, we consider a synchronized duty-cycled M2M network composed of a large number of devices that periodically wake up their radio interfaces to transmit data to a coordinator. We theoretically analyze the delay and energy efficiency of two contention-based Medium Access Control (MAC) protocols for this kind of networks. One protocol is based on Frame Slotted ALOHA, and the other is based on a tree-splitting contention resolution algorithm. Computer-based simulations have been carried out to ensure the accuracy of the theoretical models and to evaluate and compare the performance of the two alternatives for M2M applications based on low power Wi-Fi devices.

Emerging Machine-to-Machine (M2M) applications demand small data packet sizes, very low latencies, and ultrahigh energy efficiencies. For all these reasons, Binary Bose-Chaudhuri-Hocquenhem (BCH) codes, which are very simple to implement, could constitute a good option to guarantee the required reliability of M2M transmissions. Nevertheless, existing delay and energy analyses of BCH decoders in the literature neither consider the channel statistics nor the first and second moments of the decoding delay. Therefore, they provide conservative codeword designs that lead to high delays and waste of energy. In this paper, we analyze the first and second moments of the delay and energy performance of Binary Bose-Chaudhuri-Hocquenhem (BCH) codes in realistic channel statistics to show that, if optimized, they can perform very efficiently for M2M transmissions. The results presented in this paper allow for the codeword length optimization for BCH codes given specific delay and energy constraints.

Due to propagation and interference effects at the 2.4 GHz band, Machine-to-Machine (M2M) wireless communications based on the IEEE 802.15.4 standard typically need multi-hop communications to connect end devices with a gateway. Unfortunately, multi-hop transmissions pose some challenges that are not trivial to solve and may slow down the deployment of M2M networks. For this reason, the IEEE 802.15.4f Working Group (WG) is currently defining the specifications of a new physical layer operating at the 433 MHz band, which offers better propagation conditions and suffers from lower interference levels. In this paper, we analyze the energy consumption of single-hop and multi-hop communications at both 433 MHz and 2.4 GHz. We use realistic propagation models and accurate energy consumption models to conduct a comprehensive assessment of the energy performance at the two frequency bands. The results presented in this paper show that operating at 433 MHz instead of 2.4 GHz can significantly reduce the number of hops between the end device and the gateway, which can be translated into a reduction of the overall network energy consumption.

The design of communications protocols that exploit the availability of both cellular and short-range radio access interfaces in a same terminal can be exploited to improve the energy efficiency of wireless communications networks. Devices equipped with a cellular radio interface can act as dynamic gateways to provide energy-constrained devices in its single-hop cluster with cellular connectivity. For this purpose, any device may discover its single-hop neighbourhood in order to select the appropriate gateway candidates. Conventional neighbour discovery algorithms have a great cost in terms of delay and energy consumption, and thus they are not the optimal approach for energy-constrained devices. This paper presents the energy consumption analysis of two distributed neighbour discovery algorithms based on frame slotted-ALOHA, and evaluates their performance in terms of energy consumption. In addition, it describes the conditions that minimize the energy consumption in the discovery process.