In the last few years, many routing protocols have been proposed for vehicular ad hoc networks (VANETs) because of their specific characteristics. Protocols that use several metrics have been shown to be the most adequate to VANETs due to their effectiveness in dealing with dynamic environment changes due to vehicle mobility. Metrics such as distance, density, link stability, speed, and position were selected by the authors for the best proposal. Several surveys of routing proposals have been generated to categorize contributions and their application scenarios, but none of them focused on multimetric approaches. In this paper, we present a review of the routing protocols based on more than one metric to select the best route in a VANET. The main objective of this research was to present the contemporary most frequently used metrics in the different proposals and their application scenarios. This review helps in the selection protocols or the creation of metrics when a new protocol is designed.This survey of multimetric VANET routing protocols employed systematic literature-review (SLR) methodology in four well-knownown databases that allowed to analyze current state-of-the-art proposals. In addition, this paper provides a description of these multimetric routing protocols. Our findings indicate that distance and speed are the most popular and versatile metrics. Finally, we define some possible directions for future research related to the use of this class of protocols.

Vehicular ad hoc networks (VANETs) leverage the communication system of Intelligent Transportation Systems (ITS). Recently, Delay-Tolerant Network (DTN) routing protocols have increased their popularity among the research community for being used in non-safety VANET applications and services like traffic reporting. Vehicular DTN protocols use geographical and local information to make forwarding decisions. However, current proposals only consider the selection of the best candidate based on a local-search. In this paper, we propose a generic Geographical Heuristic Routing (GHR) protocol that can be applied to any DTN geographical routing protocol that makes forwarding decisions hop by hop. GHR includes in its operation adaptations simulated annealing and Tabu-search meta-heuristics, which have largely been used to improve local-search results in discrete optimization. We include a complete performance evaluation of GHR in a multi-hop VANET simulation scenario for a reporting service. Our study analyzes all of the meaningful configurations of GHR and offers a statistical analysis of our findings by means of MANOVA tests. Our results indicate that the use of a Tabu list contributes to improving the packet delivery ratio by around 5% to 10%. Moreover, if Tabu is used, then the simulated annealing routing strategy gets a better performance than the selection of the best node used with carry and forwarding (default operation).

The Internet of Things (IoT) paradigm is growing, affecting human life and aiming to solve problems in the real world, i.e., in education, healthcare, smart homes, intelligent transportation, and other areas. However, it is a fact that the development of IoT systems is complicated compared to that of traditional software systems, especially in relation to requirements engineering (RE). The RE of IoT systems is not implemented frequently due to their broad aspects, such as the variety of user needs, making these systems difficult to construct. In this sense, the use of loT-based systems has not been well explored by the research community in order to provide well-planned proposals to improve the quality of their performance. In this work, we present a comprehensive and inclusive review of the RE of loT-based systems. To accomplish this, a systematic mapping study (SMS) is presented to evaluate the use of parameters based on the existing literature. SMS is a methodology used for research in the medical field and has recently been implemented in software engineering (SE) to sort and organize research publications to gain knowledge on progress and identify research gaps. In this article, we aim to classify the existing research publications in the current scientific literature regarding RE proposals for IoT software systems and review their implications for future research. This will make it possible to establish lines of research in order to improve the quality of the development of future IoT systems.

Buildings are important elements of cities for VANETs, since these obstacles may attenuate communications between vehicles. Consequently, the impact of buildings has to be considered as part of the attenuation model in VANET simulations of urban scenarios. However, the more elaborated the model, the more information needs to be processed during the simulation, which implies longer processing times. This complexity in simulations is not always worth it, because simplified channel models occasionally offer very accurate results. We compare three approaches to model the impact of buildings in the channel model of simulated VANETs in two urban scenarios. The simulation results for our evaluation scenarios of a traffic-efficiency application indicate that modeling the influence of buildings in urban areas as the total absence of communication between vehicles gives similar results to modeling such influence in a more realistic fashion and could be considered a conservative bound in the performance metrics.

The effective administration of lithium-ion batteries is key to the performance and durability of electric vehicles (EVs). This systematic mapping study (SMS) thoroughly examines optimization methodologies for battery management, concentrating on the estimation of state of health (SoH), remaining useful life (RUL), and state of charge (SoC). The findings disclose various methods that boost the accuracy and reliability of SoC, including enhanced variants of the Kalman filter, machine learning models like long short-term memory (LSTM) and convolutional neural networks (CNNs), as well as hybrid optimization frameworks that combine Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). For estimating SoH, prevalent data-driven techniques include support vector regression (SVR) and Gaussian process regression (GPR), alongside hybrid models merging machine learning with conventional estimation techniques to heighten predictive accuracy. RUL prediction sees advancements through deep learning techniques, especially LSTM and gated recurrent units (GRUs), improved using algorithms such as Harris Hawks Optimization (HHO) and Adaptive Levy Flight (ALF). This study underscores the critical role of integrating advanced filtering techniques, machine learning, and optimization algorithms in developing battery management systems (BMSs) that enhance battery reliability, extend lifespan, and optimize energy management for EVs. Moreover, innovations like hybrid models and synthetic data generation using generative adversarial networks (GANs) further augment the robustness and precision of battery management strategies. This review lays out a thorough framework for future exploration and development in the optimization of EV batteries.

Conversational agents are a piece of software widely used nowadays in several domains, such as e-commerce, customer support, education, among others. To build one, proficiency in distinct areas of knowledge is necessary. Moreover, its development demands the availability of frameworks, tools, and proposals to facilitate its application in several domain areas. Recently, the adoption of models in a software development process has increased through the application of Model-Driven Development in several areas to improve and increase productivity in software development. This work aims to provide state of the art through a Systematic Mapping Study regarding to Model-Driven Development approaches to automate or semi-automate the development of Chatbots. For this study, 429 scientific articles were analyzed, of which, after the inclusion and exclusion criteria, only 20 were considered primary studies.