The convergence of wireless communications and transportation technologies has propelled the evolution of Intelligent Transportation Systems (ITS), enhancing connectivity, safety, and efficiency in vehicular networks. As electric vehicle (EV) adoption grows within connected ecosystems, the need for intuitive charging management solutions has surged. This paper presents a task-oriented, bilingual chatbot to optimize EV charging control via an intelligent human-machine interface (HMI). Using Natural Language Processing (NLP) and Artificial Intelligence (AI), the system enables seamless interaction with charging infrastructure through wireless channels, supporting English and Spanish to address linguistic diversity. Integrated with ITS frameworks, it facilitates real-time monitoring and control, aligning with cooperative sensing and user experience goals. Two approaches are assessed: Voiceflow, for fast prototyping, and BERT (Bidirectional Encoder Representations from Transformers)-based models, implemented as a multilingual system and separate monolingual variants. Results show monolingual BERT models outperform the multilingual model in terms of intent classification accuracy, reinforcing reliability in dynamic settings. By linking wireless connectivity with AI-driven HMI, our proposal enhances EV charging scalability and accessibility within ITS, advancing sustainable transportation and efficient vehicular networking.

Vehicular networks have transformed Intelligent Transportation Systems (ITS) by enabling Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications, enhancing road safety and traffic efficiency. However, these technologies face significant challenges in rural and mountainous environments, where communication infrastructure is limited. In this context, LoRaWAN emerges as a viable solution to extend coverage in vehicular networks, providing connectivity in remote areas where traditional solutions, such as LTE-V2X and 5G-V2X, are insufficient due to their reliance on dense infrastructure.This paper evaluates the use of LoRaWAN in V2I communications in remote areas using FresSim, a simulation tool based on Fresnel Zone analysis and geospatial data. In particular, LoRaWAN coverage has been evaluated considering the strategic placement of gateways, achieving connectivity extension up to 140 km with only five gateways. The results confirm that LoRaWAN enables the transmission of emergency alerts and vehicular data in hard-to-reach environments with minimal infrastructure, optimizing resource utilization and providing an efficient alternative to enhance V2I connectivity on rural roads and highways.

This article introduces the LoRaWAN Fresnel Zone Simulator (FresSim), a tool designed to evaluate the feasibility of LoRaWAN deployments by analyzing the Fresnel Zone and terrain topography, allowing for the early prediction of coverage between nodes and the gateway. Furthermore, unlike other tools, FresSim can integrate real data from platforms such as The Things Network (TTN) and ChirpStack, providing an accurate assessment of potential coverage and signal quality in real deployments. Comparisons between FresSim and LoRaSim, a widely used signal propagation simulator, reveal that FresSim offers superior accuracy in scenarios with complex topography, achieving a 100% success rate in predicting coverage, doubling the 50% accuracy rate of LoRaSim. These results demonstrate that FresSim is a valuable tool for optimizing LoRaWAN networks in open environments, significantly contributing to the improvement of LoRaWAN deployment planning.

The rapid adoption of electric vehicles (EVs) demands advanced residential charging solutions, where user behavior varies widely due to factors like electricity tariffs and weather conditions. Such conditions make charging predictions particularly complex. This study addresses this challenge by predicting the time an EV remains connected to a residential charger using a dataset of 106,260 sessions. To this end, we propose the Connection Time Neural Model (CTNM), a deep neural network designed to model the complex dynamics of domestic charging, and we introduce the Weighted Error Metric (WEM), a novel metric that penalizes overestimations and under-estimations differently to reflect their real-world impacts on both grid management and user experience. Utilizing bidirectional charger data, we focus solely on connection time, bypassing energy prediction. CTNM is benchmarked against state-of-the-art methods (i.e., Random Forest, Dense Neural Network, XGBoost, and Support Vector Regression) using Mean Absolute Error, Root Mean Squared Error, and WEM as performance metrics. Results demonstrate CTNM’s superiority, reducing average error by 18%, and weighted error by 31% compared to Random Forest, thanks to its deep architecture and integration of contextual features, like variable tariffs and weather.

LoRaWAN (Long Range Wide Area Network) is a communications protocol stack based on LoRa which provides long-range connectivity and low power consumption, making it a strong candidate for Internet of Things (IoT) device connectivity. However, unlike other technologies like WiFi or Bluetooth, LoRaWAN lacks an automatic mechanism to facilitate the connection of new devices to a network, which is undoubtedly a limitation for widespread device deployment. This paper presents AutoLoRaConfig, a rule-based system designed to automate the configuration and registration of devices in LoRaWAN networks, reducing the manual intervention required, and therefore minimizing potential human errors. The proposal aims to simplify the work required by users regarding the device configuration and registration process, enabling agile and efficient large-scale deployments. Results indicate that, for a set of 100 devices, automated deployment is 136.6 times faster than the manual procedure.

The use of proprietary systems in Industry 4.0 often involves high economic costs. To address this issue, using low-cost devices with similar capabilities is becoming an increasingly popular alternative. However, these devices are prone to suffer the negative effects of electromagnetic interference (EMI) due to their placement in electrical panels alongside other electromechanical devices. To solve this problem, this article presents the autonomous Data Base Filter (aDBF). aDBF is an enhanced electromagnetic interference filtering mechanism capable of eliminating erroneous signals generated by EMI. aDBF has been specifically designed to autonomously (i.e., without the need for operator supervision or intervention) determine both the number of different product types elaborated in a production line, and the time instants when their manufacturing process starts and ends. In particular, aDBF goes through three stages: (i) pre-filtering, (ii) product change detection, and (iii) identification of valid signals. The results obtained after validating our proposal in three different manufacturing shifts demonstrate that the aDBF filtering mechanism works very accurately, as the maximum error introduced is of 0.93%.

Long Range Wide Area Network (LoRaWAN) is a network protocol specifically designed to leverage the advantages of Long Range technology. LoRaWAN is employed to connect Internet of Things devices through a long-range network infrastructure, providing a secure and efficient communication layer that facilitates connectivity for a vast number of devices in a LoRa network. The aim of this study is to assess the real-life performance of LoRaWAN in different topographical environments. In particular, two scenarios (one flat and one mountainous) are compared, with an analysis of the success rate in data packet reception. The purpose is to examine how topography influences communication between LoRa devices and gateways, as well as to explore alternatives for overcoming challenges in rugged terrain environments. Therefore, in the mountainous environment, the study also investigates whether the use of drones could enhance communications. The findings of this study reveal that the topography of the terrain significantly impacts LoRa communications, resulting in a 58.63% decrease in the number of successfully received packets compared to a flat environment. Moreover, one-third of the nodes failed to establish communication with the ground gateway, and a partial improvement in connectivity was achieved by using gateways deployed on drones.

The development and deployment of the so-called Industrial Internet of Things (IIoT) have significantly increased the control and monitoring capabilities of companies, and thus their potential productivity. In this paper, we propose the use of Raspberry Pi devices in industrial environments to mea-sure productivity parameters. Our proposal can economically and efficiently gather data related with the availability and productivity of industrial machinery. However, since low-cost devices are prone to suffer the negative effects of electromagnetic interferences, we additionally propose an alternative to prevent signal alterations caused by them. More specifically, we propose a filtering mechanism called Smart Coded Filter (SCF), which eliminates wrong signals caused by electromagnetic interferences, and, therefore, highly improves the accuracy when estimating the availability metric. Results obtained demonstrate that our low-cost device provided with the SCF completely ignores 100% of wrong availability data, while reducing up to 70% the number of records stored into the database.

In this paper we present V-tracer, a vehicular trace generator aimed at generating realistic data about mobility of vehicles, as well as their daily operation and wear. The objectives of our approach are two: first, gathering real traces obtained by in-vehicle on-board units (OBUs), and second, as the first target is hard to achieve, generating synthetic data. The final goal will be getting all the information that would be very useful to infer and predict vehicle failures. The traces provided by our generator may be used to perform the predictive maintenance of vehicles in the near future.

In this paper, we present the Dynamic Small cell Management (DSM) scheme to improve vehicular communications, focusing on the dynamic allocation of small cells when the macrocells cannot cope with the traffic generated by the connected cars. We have considered real base station deployments in the city of Dublin, Ireland, combined with realistic models of vehicle mobility, and small cell deployments. Simulation results demonstrate that our DSM scheme improves communication capabilities (the number of messages correctly received by the infrastructure increases up to a 43.75%), while it also reduces BS overloading (the number of messages managed by the base stations is reduced up to an 8.72%). Therefore, the use of this smart and dynamic solution not only benefits vehicles' communications but also mobile operators.

Emerging ITS applications such as fleet management and point of interest distribution require vehicles to have Internet access. However, allowing vehicles to access to the Internet is particularly challenging due to the special characteristics of the vehicular environment. So far, multicasting approaches have been demonstrated to be effective for supporting group communication in traditional networks. However, such Internet-to-VANET multicast service involves several challenges including efficient multicast mobility management and multicast message delivery. This paper proposes a scheme that combines the existing multicast mobility management scheme with vehicular networking solutions to achieve Internet-to-VANET multicasting. The proposed scheme aims to: (i) provide multicast mobility management with low control overhead and efficient bandwidth utilization, as well as (ii) extend the service coverage provided by VANET membership management and multicast message delivery protocol. Simulation results indicate that our Motion-MAODV scheme improves the performance of both MAODV and traditional flooding dissemination schemes in terms of both packet delivery ratio and end-to-end transmission latency.

In this paper, we study the influence of the roadmap topology and the number of vehicles when accounting for the vehicular communications capabilities, especially in urban scenarios. Additionally, we propose the use of two metrics: the SJ Ratio (SJR) and the Total Distance (TD), as the metrics that better correlate with communications performance. Hence, researchers will better characterize the different urban scenarios. In particular, simulation results demonstrate that roadmaps with both similar SJR and TD present similar communications performance in terms of vehicles informed.

Research works regarding vehicular communications usually obviate assessing the proposals in scenarios including adverse vehicle densities, despite such scenarios are quite common in real urban environments. In this paper, we study the effect of these hostile conditions on the performance of different schemes providing warning message dissemination. We then propose the Junction Store and Forward (JSF) and the Nearest Junction Located (NJL) schemes, which were specially designed to be used in very low and very high density scenarios, respectively. Simulation results using real maps demonstrate how our proposed schemes are able to outperform existing warning message dissemination schemes in urban environments under adverse vehicle density conditions.

New communication technologies integrated into modern vehicles offer an opportunity for better assistance to people injured in traffic accidents. Recent studies show how communication capabilities should be supported by artificial intelligence systems capable of automating many of the decisions to be taken by emergency services, thereby adapting the rescue resources to the severity of the accident and reducing assistance time. To improve the overall rescue process, a fast and accurate estimation of the severity of the accident represent a key point to help emergency services better estimate the required resources. This paper proposes a novel intelligent system which is able to automatically detect road accidents, notify them through vehicular networks, and estimate their severity based on the concept of data mining and knowledge inference. Our system considers the most relevant variables that can characterize the severity of the accidents (variables such as the vehicle speed, the type of vehicles involved, the impact speed, and the status of the airbag). Results show that a complete Knowledge Discovery in Databases (KDD) process, with an adequate selection of relevant features, allows generating estimation models that can predict the severity of new accidents. We develop a prototype of our system based on off-the-shelf devices and validate it at the Applus+ IDIADA Automotive Research Corporation facilities, showing that our system can notably reduce the time needed to alert and deploy emergency services after an accident takes place.

A critical issue, especially in urban areas, is the occurrence of traffic accidents, since it could generate traffic jams. Additionally, these traffic jams will negatively affect to the rescue process, increasing the emergency services arrival time, which can determine the difference between life or death for injured people involved in the accident. In this paper, we propose four different approaches addressing the traffic congestion problem, comparing them to obtain the best solution. Using V2I communications, we are able to accurately estimate the traffic density in a certain area, which represents a key parameter to perform efficient traffic redirection, thereby reducing the emergency services arrival time, and avoiding traffic jams when an accident occurs. Specifically, we propose two approaches based on the Dijkstra algorithm, and two approaches based on Evolution Strategies. Results indicate that the Density-Based Evolution Strategy system is the best one among all the proposed solutions, since it offers the lowest emergency services travel times.

Efficient schemes for warning message dissemination in vehicular ad hoc networks (VANETs) use context information collected by vehicles about their neighbor nodes to guide the dissemination process. These schemes maximize their performance when all the vehicles advertise correct information about their positions, and hence position errors may drastically reduce the performance of the dissemination process. We present a proactive Cooperative Neighbor Position and Verification (CNPV) protocol that detects nodes advertising false locations so as to mitigate the impact of adversarial users. We combine our mechanism with two warning dissemination schemes for VANETs, and demonstrate how these algorithms can benefit from the use of our security scheme in the presence of malicious nodes trying to exploit the inherent vulnerabilities of each algorithm.

In Vehicular ad hoc Networks (VANETs), efficient dissemination of messages is a key factor to speed up the development of useful services and applications. In this paper, we propose a novel algorithm that automatically chooses the best dissemination scheme trying to fit the warning message delivery policy to the current characteristics of each specific vehicular scenario. Our mechanism uses as input parameters the vehicular density and the topological characteristics of the environment where the vehicles are located, in order to decide which dissemination scheme to use. Simulation results demonstrate the feasibility of our approach, which is able to support more efficient warning message dissemination in vehicular environments.

Vehicle density is one of the main metrics used for assessing the road traffic conditions. In this paper, we present a solution to estimate the density of vehicles that has been specially designed for Vehicular Networks. Our proposal allows Intelligent Transportation Systems to continuously estimate the vehicular density by accounting for the number of beacons received per Road Side Unit, as well as the roadmap topology. Simulation results indicate that our approach accurately estimates the vehicular density, and therefore automatic traffic controlling systems may use it to predict traffic jams and introduce countermeasures.

In recent years, new applications, architectures, and technologies have been proposed for vehicular ad hoc networks (VANETs). Regarding traffic safety applications for VANETs, warning messages have to be quickly and smartly disseminated in order to reduce the required dissemination time and to increase the number of vehicles receiving the traffic warning information. In the past, several approaches have been proposed to improve the alert dissemination process in multihop wireless networks, but none of them were tested in real urban scenarios, adapting its behavior to the propagation features of the scenario. In this paper, we present the Profile-driven Adaptive Warning Dissemination Scheme (PAWDS) designed to improve the warning message dissemination process. With respect to previous proposals, our proposed scheme uses a mapping technique based on adapting the dissemination strategy according to both the characteristics of the street area where the vehicles are moving and the density of vehicles in the target scenario. Our algorithm reported a noticeable improvement in the performance of alert dissemination processes in scenarios based on real city maps.

Nowadays, protection of personal data is an important issue when developing software. In 1999, the Organic Law of Personal Data (LOPD) was passed in Spain. According to this Law, every computer application which works with personal data has to be adapted to be in accordance with the LOPD requirements. However, there is no doubt that many personal data is currently improperly managed, especially by education centers and professors. This article presents GADM (Gestor de Alumnos para Dispositivos Móviles), an ad-hoc free software specially designed to help teachers to manage and monitor school information about the students' progress, their evaluation marks, etc. Moreover, GADM offers secure communications and appropriate management of such kind of data in educational environments. Our application has been tested by several teachers at the secondary education stage, with very satisfactory results.

In a near future, vehicles will be provided with a variety of new sensors capable of gathering information from their surroundings. These vehicles will also be capable of sharing the harvested information via Vehicular Ad hoc NETworks (VANETs) with nearby vehicles, or with the emergency services in case of an accident. Hence, distributed applications based on VANETs will need to agree on a `common understanding' of context for interoperability, and therefore, it is necessary to create a standard structure which enables data interoperability among all the different entities involved in transportation systems. In this paper, we focus on traffic safety; specifically, we present a Car Accident lightweight Ontology for VANETs (CAOVA). The instances of our ontology are filled with: (i) the information collected when an accident occurs, and (ii) the data available in the General Estimates System (GES) accidents database. We assess the reliability of our proposal in two different ways: one via realistic crash tests, and the other one using a network simulation framework.

In recent years, new applications, architectures and technologies have been proposed for Vehicular ad hoc networks (VANETs). Regarding traffic safety applications for VANETs, warning messages have to be quickly disseminated in order to reduce the required dissemination time and to increase the number of vehicles receiving the traffic warning information. In the past, several approaches have been proposed to improve the alert dissemination process in multi-hop wireless networks, but none of them is adapted to the propagation features of the scenario. In this paper, we present an adaptive algorithm designed to improve the warning message dissemination process. With respect to previous proposals, our proposed scheme uses a mapping technique based on adapting the dissemination strategy according to the characteristics of the street area where the vehicles are moving. Our algorithm reported a noticeable improvement in the performance of alert dissemination processes in simulated scenarios based on real city maps.

In recent years, new architectures and technologies have been proposed for Vehicular ad hoc networks (VANETs).However, the experiments to validate these proposals tend to overlook the most important and representative factors. Moreover, the scenarios simulated tend to be very simplistic (highways or Manhattan-based layouts), which could seriously affect the validity of the obtained results. In this paper, we present a statistical analysis based on the 2kfactorial methodology to determine the most representative factors affecting traffic safety applications under real roadmaps. Our purpose is to determine which are the key factors affecting Warning Message Dissemination (WMD) in order to concentrate on such parameters, thus reducing the amount of simulation time required. Simulation results show that the key factors affecting warning messages delivery are the density of vehicles, and the roadmap used. Based on this statistical analysis, we consider that VANET researchers must evaluate the benefits of their proposals using different vehicle densities and city scenarios.

In traffic safety applications for Vehicular Ad hoc Networks (VANETs), warning messages have to be disseminated whenever a dangerous situation occurs to alert nearby vehicles. Using inefficient broadcast schemes may lead to ineffective dissemination of warning messages causing broadcast storm problems. In the past, several approaches have been proposed to reduce the so called broadcast storm in multi-hop wireless networks, but none of them is adapted to the features of the propagation scenario. In this paper, we present the Profile-driven Adaptive Warning Dissemination Scheme (PAWDS) to improve the warning message dissemination process. With respect to previous proposals, our PAWDS scheme uses an adaptive technique based on tuning the operation of the dissemination scheme according to the characteristics of the street area where the vehicles are moving. Our algorithm reported a noticeable improvement in the performance of alert dissemination processes in simulated scenarios based on real city maps.