This work was supported by the project ‘‘[DISCOVERY]: Distributed Smart Communications with Verifiable EneRgy-optimal Yields’’ PID2023-148716OB-C32 (Agencia Estatal de Investigación, Spain, Ministerio de Ciencia e Innovación); also by the project ‘‘MultiMO: Datos MultiSectoriales para la Movilidad Obligada’’ TSI-100123-2024-60 (Ministerio de transformación digital y de la función pública, NextGenerationEU); also, by predoctoral scholarship for the training of research personnel associated with the ‘‘Generación de Conocimiento’’ Project PRE2021-099830.

This paper evaluates a Deep Q-Network (DQN_CC) based congestion control mechanism for multi-hop mobile wireless networks, extending its previous evaluation in static Wireless Smart Grid environments to scenarios characterized by node mobility and dynamic topologies. Unlike traditional Q-learning approaches that rely on discrete Q-tables and cope with large state spaces, DQN_CC employs deep reinforcement learning to generalize across states, enabling real-time regulation of packet transmissions based on observations of global and local packet delivery ratios (PDRs) without the need for pre-labeled datasets. Simulation campaigns with networks of different sizes and mobility patterns demonstrate that DQN_CC consistently improves key performance metrics, achieving higher PDR, lower end-to-end delays, and higher throughput compared to configurations without congestion control. While slightly moderating the overall throughput delivered to mitigate congestion, DQN_CC ensures that a higher proportion of packets meet latency constraints, highlighting its effectiveness in maintaining reliable and timely communication in challenging multi-hop mobile wireless scenarios.

Security assurance is the capacity of any teleinformatic system to demonstrate that the system is secure. It is provided by testing the system by an independent laboratory. Such an evaluation gives to the customer certainty that the system or product is secure enough for the intended use. In this paper we discuss the security assurance that the automotive sector requires to the 5G network for the secure communication of the Intelligent Transport System applications. In this case, the automotive sector takes the role of the customer of the network operator that provides connectivity of cars, trucks, bicycles, pedestrians and traffic infrastructure for creating vehicle to everything platform. Concretely, in this paper we (1) show new methodologies for evaluating network security, (2) provide a new strategy for the customer (automotive company) to select the underlying system based on network assurance levels, (3) survey security functionalities of a network devoted to automotive applications and demonstrate how automotive applications, 5G network and physical infrastructure, cooperate for enhancing security of the end-to-end system, and (4) provide example security evaluation results at different assurance levels. The results show the necessity of providing different levels of network assurance during the certification process in order that the automotive customer will be able to select the best products and sub-systems that may demonstrate (assure) enough security to the complete system.

In this paper, we propose different strategies to efficiently deploy RSUs in a city with the ultimate goal of having an 802.11p-based infrastructure to deliver Internet services. Unlike most existing works, (i) our strategies' only prior information is the average density of vehicles in the studied area, and (ii) they rely on the forecast of a performance model of 802.11p to assist and guide their choices regarding the location of RSUs. With the help of two simulators, namely SUMO and ns-3, we investigate the behavior of each strategy in three scenarios inspired by the street map of real-life major cities. Our findings are twofold: (i) we demonstrate that any efficient RSUs deployment is tightly tied to the specifics of the considered city (namely, the arrangement of streets and the spatial density of vehicles); (ii) the best strategy is not to position RSUs where the traffic density is at its highest, nor at the street junctions where the traffic density is often at its highest but instead where they will be able to deliver the target QoS to a maximum number of vehicles.

The application areas of multi-hop wireless networks are expected to experience sustained growth in the next years. This growth will be further supported by the current possibility of providing low-cost communication capabilities to any device. One of the main issues to consider with this type of networks is congestion control, that is, avoiding an excessive volume of data traffic that could lead to a loss of performance. In this work, a distributed congestion control mechanism is proposed for generic multi-hop networks. Different categories of data traffic are taken into account, each of them with different quality of service requirements. The mechanism is based on machine learning techniques, specifically, the CatBoost algorithm that uses gradient boosting on decision trees. The obtained decision trees are used to predict whether the packets to be transmitted over the network will reach their destination on time or not. This prediction will be made based on the network load state, which will be quantified by means of two parameters: the utilization factor of the different transmission channels, and the occupancy of the buffers of the network nodes. To make the values of these parameters available to all nodes in the network, an appropriate dissemination protocol has also been designed. Besides, a method to assign different transmission priorities to each traffic category, based on the estimation of the network resources required at any time, has also been included. The complete system has been implemented and evaluated through simulations, which show the correct functionality and the improvements obtained in terms of packet delivery ratio, network transit time, and traffic differentiation.