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.

Electric vehicles (EVs) offer significant potential for reducing emissions, yet their expansion is constrained by long charging times, limited charging infrastructure, and inefficient charging station (CS) utilization. This study proposes an intelligent platform that explicitly supports drivers in multiple aspects: predicting EV energy consumption (EVEC), estimating the remaining energy at the destination, and determining the remaining driving range, thereby assisting drivers in deciding whether to continue their trip or stop for recharging. The system also recommends the most appropriate CS by considering driver preferences. Using SUMO simulations with OpenStreetMap data to prepare realistic traffic scenarios, the platform combines ensemble machine learning (ML) models with federated learning (FL) to optimize EV charging decisions. Through the integration of Bi-LSTM + XGBoost for EVEC prediction and FFNN + XGBoost for the optimal CS selection, the proposed framework significantly outperforms conventional methods by minimizing total travel time and resulting in mean absolute error (MAE) values ranging from 2.3 to 4.5 minutes under varying traffic conditions.

The coordinated scheduling of electric vehicle (EV) charging is a critical challenge for smart cities, particularly in high-density infrastructure such as Mobility Hubs (MHs). This paper evaluates and compares two prominent approaches to the EV Charging Scheduling Problem (CSP): Mixed-Integer Linear Programming (MILP) and Reinforcement Learning (RL). We formulate a shared problem framework and apply both strategies under two structured scenarios: a small-scale deterministic benchmark and a medium-scale, realistic deployment with higher heterogeneity. Results show that MILP achieves optimal cost and state of charge SoC compliance in tractable cases but struggles with scalability. RL, based on Proximal Policy Optimization (PPO), achieves near-optimal performance while scaling to 100 EVs with minimal computation time. Despite occasional SoC deviations, the RL agent exhibits robust and adaptive behavior under dynamic conditions. This study offers actionable insights for selecting and deploying EV scheduling strategies in real-world urban environments.

This research addresses the challenges of urban mobility and logistics by developing an AI-based system for intelligent route prediction and coordination. Using Graph Neural Networks and complementary AI methods, we intend to model heterogeneous fleets to optimize large-scale mobility with real-time decision-making and energy-aware trade-offs.

Modern urban mobility systems are increasingly dependent on precise and scalable simulation platforms to facilitate the design, assessment, and optimization of intelligent transportation systems (ITS). This paper sets forth a thorough benchmarking investigation of three extensively utilized traffic simulation platforms: SUMO (Simulation of Urban MObility), CityFlow, and MATSim, with an emphasis on their computational efficacy within large-scale synthetic traffic scenarios. We conduct an evaluation of these platforms across vehicle volumes ranging from 10 to 10 million vehicles, assessing total runtime, CPU and GPU utilization, and memory consumption on high-performance computing infrastructures. Our results reveal notable architectural trade-offs: SUMO exhibits predictable linear scaling but becomes constrained by CPU limitations at elevated vehicle counts, CityFlow encounters memory limitations beyond 10,000 vehicles, whereas MATSim necessitates meticulous JVM (Java Virtual Machine) optimization to effectively manage large-scale scenarios. This investigation provides essential guidance for researchers and practitioners in the selection of suitable simulation tools for urban-scale traffic modeling and identifies critical computational challenges associated with the scaling of simulations for smart city initiatives.

Non-technical losses represent a significant challenge for energy distribution companies due to their economic impact. These losses typically arise from irregularities at supply points and fraudulent customer behavior. In Cuba, electricity meter readings are performed manually, and consumption data is processed using spreadsheets that combine weighted criteria to generate alerts for potential anomalies. This procedure is prone to vulnerabilities such as manual data entry errors, incorrect key assignments, and human mistakes made by field readers, which compromise the reliability of the analysis. In this paper we present an anomaly detection system for electricity consumption, developed using artificial intelligence techniques to identify irregularities based on monthly reports from residential users. Various machine learning methods were evaluated, with eXtreme Gradient Boosting (XGBoost) standing out for its effectiveness in handling imbalanced datasets. Additionally, a web application was implemented using Flask to process consumption data and provide real-time predictions, optimizing the management of nontechnical losses. The results confirm that, with real consumption data, the algorithms achieve high accuracy in fraud detection, even in scenarios with severe class imbalance, validating the robustness of the system. Beyond its application in the energy sector, this solution is adaptable to other contexts requiring anomaly detection in transactional data. This proposal contributes to reducing economic losses, improving operational efficiency, and laying the groundwork for future research in intelligent monitoring systems.

Traffic congestion in urban areas has intensified due to the rapid growth of vehicles, inadequate infrastructure planning, and unsynchronized traffic signals. This study presents an adaptive traffic signal control strategy called Gap-Fuzzy, which combines the Mamdani fuzzy logic controller with a gapout detection mechanism. The system dynamically adjusts the duration of the green light based on real-time traffic data, including queue length and arrival rate. Furthermore, the green phase is terminated early if prolonged gaps in vehicle flow are detected. We evaluated the proposed Gap-Fuzzy system using the SUMO microscopic traffic simulator. The results indicate that it reduced vehicle waiting times by up to 70% and CO2emissions by 25% compared to fixed-time control. In addition, it outperformed the SUMO actuated controller under low, medium, and high traffic conditions while maintaining comparable performance under very high demand. These findings highlight the effectiveness of integrating fuzzy logic with gap-out detection to enhance traffic flow and minimize environmental impact.

We are witnessing a profound shift in societal and political attitudes, driven by the visible consequences of climate change in urban environments. Urban planners, public transport providers, and traffic managers are urgently reimagining cities to promote sustainable mobility and expand green spaces for pedestrians, bicycles, and scooters. To design more sustainable cities, urban planners require realistic simulation tools to optimize mobility, identify location for car chargers, convert streets to pedestrian zones, and evaluate the impact of alternative configurations. However, realistic traffic profiles are essential to produce meaningful simulation results. Addressing this need, we propose a traffic generator based on deep reinforcement learning integrated with the SUMO simulator. This tool learns to generate an instantaneous number of vehicles throughout the day, aligning closely with the target profiles observed at the traffic monitoring stations. Our approach generates accurate 24-hour traffic patterns for any city using minimal statistical data, achieving higher accuracy compared to existing alternatives. In particular, our proposal demonstrates a highly accurate 24-hour traffic adjustment, with the generated traffic deviating only by about 5% from the real target traffic. This performance significantly exceeds that of current SUMO tools like RouteSampler, which struggle to accurately follow the total daily traffic curve, especially during peak hours when severe traffic congestion occurs.

Location privacy is a critical aspect of vehicular networks. Privacy schemes adopt diverse methods to minimize vehicle traceability. These schemes include pseudonym periodical changes, silent periods, context-based approaches, and cooperative pseudonym changes. It is important to maintain a discontinuity in the information sent by the vehicle to avoid reconstructing the vehicle trace. In this paper, we propose three privacy schemes that use SLOW as a baseline and combine it with other techniques: SLOW-based random silent periods, SLOW-based cooperative pseudonym change, and SLOW-based context-aware privacy scheme. These proposed schemes aim to enhance the original scheme by restricting their operations with intervals and thresholds and improving the management of silent periods. We evaluate the proposed schemes' performance in terms of traceability, computation time, pseudonym usage, and silence time. Results show that our schemes improve the original scheme in most tested metrics. The average improvement in traceability is 70%. Furthermore, our schemes reduce the silent period of the SLOW scheme by 17.40% on average.

The particular requirements and special features of vehicular ad hoc networks (VANETs) (e.g., special mobility patterns, short link lifetimes, rapid topology changes) involve challenges for the research community. One of these challenges is the development of new routing protocols specially designed for VANETs. In this paper, we present a novel game-theoretical approach of a multimetric geographical routing protocol for VANETs to forward video-reporting messages in smart cities. Game theory is considered a very interesting theoretical framework to analyze and optimize resource allocation problems in digital communication scenarios. Our contribution has shown to enhance the overall performance of VANETs in urban scenarios, in terms of percentage of packet losses, average end-to-end packet delay and peak signal to noise ratio (PSNR).

The Third Generation Partnership Project (3GPP) has recently announced its Release 16, which introduces advanced functionalities to support the cellular vehicle to everything (C-V2X) technology. C-V2X allows direct communication between vehicles through the sidelink operation. This appears as an option to DSRC technologies; both are key wireless technologies that play a vital role in implementing and deploying advanced driving applications. This paper presents a thorough review of the available open-source frameworks intended for the performance evaluation of C-V2X protocols and V2X applications. For this, we consider validated OMNeT++-based simulation libraries and frameworks: SimuLTE, OpenCV2X, Artery-C, 5G-Sim-V2I/N, and Simu5G. We focus on the different frameworks' support regarding advanced V2X communications on 5G mobile networks.

Simulations are the first approach used by the research community to evaluate mobile ad hoc networks. Particularly, vehicular ad hoc networks (VANETs) are a singular type of mobile ad hoc networks that raise technical challenges, for instance, in the context of vehicular mobility models. When assessing VANETs, realistic vehicular models are essential to produce meaningful evaluation results. In this context, realistic vehicles' mobility includes re-routing capabilities that allow vehicles to re-compute their routes in front of specific traffic conditions (e.g., traffic jams). In this paper, we provide a thorough analysis of the influence of enabling re-routing properties on (i) the mobility of the vehicle and (ii) on the connections of the vehicular network. For this, we use the road traffic simulator SUMO to generate vehicular traces, and then we will analyze the connectivity of the vehicular network employing well-known graph metrics. Our results provide insights about the behavior of the vehicle's mobility and the nodes' connectivity.

The design of different metrics in hop-by-hop routing protocols for vehicular ad hoc networks (VANETs) has been investigated with the clear objective to improve the performance of VANETs in terms of packet losses and average end-to-end delay. However, the best way of combining these metrics to compute a multiscore value for each candidate node to choose the best forwarding one, is an open issue yet. Based on a previous designed routing protocol called Multimedia Multimetric MapAware Routing Protocol (3MRP), a detailed analysis of the power mean function is done to determine which form of metrics combination will give the best results in terms of packet losses and average end-to-end delay. Extensive simulations have been conducted indicating that the geometric mean has obtained the lowest average percentage of packet losses.

Connected vehicles (CVs) are one of the critical components of intelligent transportation systems. CVs enable any vehicle to act as a smart node that collects and shares information on vehicles, roads, and their surroundings. This information can then be distributed to other vehicles via vehicle-to-vehicle (V2V) communication, and also to road users via vehicle-to-human (V2H) communication, for an improved driving experience. The information can also be forwarded toward traffic control systems via vehicle-to-infrastructure (V2I) communication, for improved traffic management and road safety. Making use of of connected vehicles in intelligent transportation systems will revolutionize the way we drive. Many issues, however, need to be resolved to achieve better performance of connected vehicles. Improvements relate to data processing and storage, the development of standards and regulations across all platforms, design and deployment of new communication protocols and system architectures, and the creation and introduction of new services and applications.

Video content delivery for vehicular ad hoc networks (VANETs) is envisioned to be of high benefit for road safety, traffic management as well as for providing value-added vehicle services. In this paper, we evaluate five existing dissemination mechanisms in a realistic urban scenario. We also propose RCP+ as a renovated mechanism for adaptative video streaming over VANETs. RCP+ is a cross layer dissemination mechanism for video safety messages that specifically addresses urban scenarios with zero infrastructure support. Through simulations, we compare our proposal with other distributed dissemination mechanisms. The performance evaluation shows that our proposal mechanism is more suitable for video transmission in realistic urban scenarios. The results also indicate that a modified frame coding approach provides many opportunities for multimedia services.

ABSTRACT Message encryption does not prevent eavesdroppers from unveiling who is communicating with whom, when, or how frequently, a privacy risk wireless networks are particularly vulnerable to. The Crowds protocol, a well‐established anonymous communication system, capitalizes on user collaboration to enforce sender anonymity. This work formulates a mathematical model of a Crowd‐like protocol for anonymous communication in a lossy network, establishes quantifiable metrics of anonymity and quality of service (QoS), and theoretically characterizes the trade‐off between them. The anonymity metric chosen follows the principle of measuring privacy as an attacker's estimation error. By introducing losses, we extend the applicability of the protocol beyond its original proposal. We quantify the intuition that anonymity comes at the expense of both delay and end‐to‐end losses. Aside from introducing losses in our model, another main difference with respect to the traditional Crowds is the focus on networks with stringent QoS requirements, for best effort anonymity, and the consequent elimination of the initial forwarding step. Beyond the mathematical solution, we illustrate a systematic methodology in our analysis of the protocol. This methodology includes a series of formal steps, from the establishment of quantifiable metrics all the way to the theoretical study of the privacy QoS trade‐off. Copyright © 2013 John Wiley & Sons, Ltd.

Road safety has become a main issue for governments and car manufacturers in the last twenty years. The development of new vehicular technologies has favoured companies, researchers and institutions to focus their efforts on improving road safety. During the last decades, the evolution of wireless technologies has allowed researchers to design communication systems where vehicles participate in the communication networks. Thus, new types of networks, such as Vehicular Ad Hoc Networks (VANETs), have been created to facilitate communication between vehicles themselves and between vehicles and infrastructure. New concepts where vehicular networks play an important role have appeared the last years, such as smart cities and living labs [1]. Smart cities include intelligent traffic management in which data from the TIC (Traffic Information Centre) infrastructures could be reachable at any point. To test the possibilities of these future cities, living labs (cities in which new designed systems can be tested in real conditions) have been created all over Europe. The goal of our framework is to transmit information about the traffic conditions to help the driver (or the vehicle itself) take adequate decisions. In this work, the development of a warning system composed of Intelligent Traffic Lights (ITLs) that provides information to drivers about traffic density and weather conditions in the streets of a city is proposed and evaluated through simulations.

Vehicular communication for intelligent transportation systems will provide safety, comfort for passengers, and more efficient travels. This type of network has the advantage to warn drivers of any event occurred in the road ahead, such as traffic jam, accidents or bad weather. This way, the number of traffic accidents may decrease and many lives could be saved. Moreover, a better selection of non-congested roads will help to reduce pollution. Some other interesting services, such as downloading of multimedia services, would be possible and available through infrastructure along the roadside. Providing multimedia services over VANETs may require a QoS-aware routing protocol that often need to estimate available resources. In this paper, we study the performance, in realistic VANET urban scenarios, of an extension of AODV that includes the available bandwidth estimator ABE. AODV-ABE establishes forwarding paths that satisfy the bandwidth required by the applications. The results, obtained on the NCTUns simulator, show that AODV-ABE could be used in urban-VANETs where vehicles' speed is moderate.

In recent years the growing proliferation of small wireless devices able to maintain wireless communications using IEEE 802.11 technologies has enabled the deployment of MANETs (Mobile Ad Hoc Networks). This fact has stimulated the demand of multimedia services over this type of networks, such as vi

Nowadays dynamic service management frameworks are proposed to ensure end-to-end QoS. To achieve this goal, it is necessary to manage service level agreements (SLA) which specify quality parameters of the services operation such as availability and performance. This work is focused on video-on-demand (VoD) services to investigate the goodness of performability techniques in end-to-end QoS scenarios. Based on a straightforward Markov chain, Markov-reward chain (MRC) models are developed in order to obtain various QoS measures of an adaptive VoD service. The MRC model has a clear understanding with the design and operation of the VoD system. In this way, several design options can be compared. To compute performability measures of the MRC model, the randomization method is employed. Predicted model results fits well with the ones taken from a real video-streaming testbed.

Because of the dramatic growth of the Internet and the requirement of quality of service (QoS) guarantees for new and future applications it is essential that the Internet ensures end-to-end QoS. To provide this level of QoS, many protocols have evolved in the last few years, and some end-to-end QoS architectures have been proposed to mix all of these protocols appropriately. Also, it is necessary to classify applications by QoS requirements to understand how they may reserve resources from the network. In this paper, we briefly review the existing end-to-end QoS protocols and an end-to-end QoS architecture that combines all of them. In addition, we classify Internet data flows by their QoS requirements. We focus our interest on the study of semi-elastic flows and the minimization of their transmission cost. First, we present a client-server system to transmit these flows while reducing the cost. Also, we analyze the buffer management required at the client. Finally, we implement the system in the Network Simulator 2 to demonstrate that our analytical study can be used in a real system in an efficient manner to minimize the cost.

In a wide area network (WAN) established infrastructure, one of the main problems ATM network planners and users face, when greater than T1/E1 bandwidth is required, is the high cost associated to T3/E3 links. The technology to cover the gap between T1/E1 and T3/E3 bandwidth at reasonable cost is known as inverse multiplexing for ATM (IMA). IMA allows multiple T1/E1 lines to be aggregated to support the transparent transmission of ATM cells over one single virtual trunk. In this paper, the fundamentals and major applications of IMA technology are described. Also, the behavior of IMA multiplexers is carefully analyzed and a method to dimension them proposed. For that purpose an IMA simulation tool has been developed, which permits the study of individual devices and the evaluation of the end-to-end performance of a logical trunk under several ATM input traffic patterns. The analytical study is based on the comparison with a M/D/C/(N+C) queue under Poisson input traffic.