The development and high growth of the Internet of Things (IoT) have improved quality of life and strengthened different areas in society. Many cities worldwide are looking forward to becoming smart. One of the most popular use cases in smart cities is the implementation of smart parking solutions, as they allow people to optimize time, reduce fuel consumption, and carbon dioxide emissions. Smart parking solutions have a defined architecture with particular components (sensors, communication protocols, and software solutions). Although there are only three components that compose a smart parking solution, it is important to mention that each component has many types that can be used in the deployment of these solutions. This paper identifies the most used types of every component and highlights usage trends in the established analysis period. It provides a complementary perspective and represents a very useful source of information. The scientific community could use this information to decide regarding the selection of types of components to implement a smart parking solution. For this purpose, herein we review several works related to smart parking solutions deployment. To achieve this goal, a semi-cyclic adaptation of the action research methodology combined with a systematic review is used to select papers related to the subject of study. The most relevant papers were reviewed to identify subcategories for each component; these classifications are presented in tables to mark the relevance of each paper accordingly. Trends of usage in terms of sensors, protocols and software solutions are analyzed and discussed in every section. In addition to the trends of usage, this paper determines a guide of complementary features from the type of components that should be considered when implementing a smart parking solution.

Smart cities exploit emerging technologies such as Big Data, the Internet of Things (IoT), Cloud Computing, and Artificial Intelligence (AI) to enhance public services management. The use of IoT allows detecting and reporting specific parameters related to different domains of the city, such as health, waste management, agriculture, transportation, and energy. LoRa technologies, for instance, are used to develop IoT solutions for several smart city domains thanks to its available features, but sometimes people (i.e., citizens, information technology administrators, or city managers) might think that these available features involve cybersecurity risks. This study explores the cybersecurity aspects that define an assessment model of cybersecurity maturity of IoT solutions to develop smart city applications. In that sense, we perform a systematic literature review based on a top-down approach of cybersecurity incident response in IoT ecosystems. Besides, we propose and validate a model based on risk levels to evaluate the IoT cybersecurity maturity in a smart city.

The New Urban Agenda (Agenda 2030) adopted at the United Nations Conference related to Sustainable Urban Development (Habitat III) in the year 2016 has the goal of prompting cities to achieve the identified Sustainable Development Goals by the year 2030. In this context, cities can experiment strategies of circular economy for the optimization of resources, waste reduction, reuse, and recycling. The data generated by the components of an Internet of Things (IoT) ecosystem can contribute in two relevant ways to a smart city model: (1) by the generation of a circular economy and (2) by the creation of intelligence to improve the decision-making processes by citizens or city managers. In this context, it is in our interest to understand the most relevant axes of the research related to IoT, particularly those based on the LoRa technology. LoRa has attracted the interest of researchers because it is an open standard and contributes to the development of sustainable smart cities, since they are linked to the concepts of a circular economy. Additionally, the intention of this work is to identify the technological or practical barriers that hamper the development of solutions, find possible future trends that could exist in the context of smart cities and IoT, and understand how they could be exploited by the industry and academy.

In the era of digitalization, the interaction between humans and machines, particularly in Natural Language Processing, has gained crucial importance. This study focuses on improving the effectiveness and accuracy of chatbots based on Natural Language Processing. Challenges such as the variability of human language and high user expectations are addressed, analyzing critical aspects such as grammatical structure, keywords, and contextual factors, with a particular emphasis on syntactic structure. An optimized chatbot model that considers explicit content and the user’s underlying context and intentions is proposed using machine learning techniques. This approach reveals that specific features, such as syntactic structure and keywords, are critical to the accuracy of chatbots. The results show that the proposed model adapts to different linguistic contexts and offers coherent and relevant answers in real-world situations. Furthermore, user satisfaction with this advanced model exceeds traditional models, aligning with expectations of more natural and humanized interactions. This study demonstrates the feasibility of improving chatbot–user interaction through advanced syntactic analysis. It highlights the need for continued research and development in this field to achieve significant advances in human–computer interaction.

Malware targeting user privacy has seen a surge in recent times, attributed to evolving global regulations and the boost of electronic commerce and online services. Moreover, recognizing privacy malware that employs obfuscation as evasion mechanism presents a major challenge due to its dynamics, resilience, and polymorphism at runtime, necessitating the application of forensic techniques such as memory dumping analysis in order to reveal suitable patterns and behaviors that enable its subsequent detection and classification. In this paper, we present three obfuscated privacy malware classifiers trained on the CIC-MalMem-2022 dataset. These solutions include a binary classifier to distinguish benign from malicious samples using logistic regression (LR), a multiclass classifier that further categorizes benign, spyware, ransomware, and trojan horse obfuscated privacy malware; and a more detailed multiclass classifier capable of discriminating benign samples from fifteen specific obfuscated privacy malware families. Multiclass classifiers were built using several traditional machine learning algorithms and a novel Deep Neural Network (DNN). We applied the Synthetic Minority Oversampling Technique (SMOTE) to address data imbalances. In particular, our results demonstrate that DNN outperforms traditional machine learning algorithms, yielding statistically significant improvements in key metrics. These achievements reach practical thresholds, suggesting the potential for enhanced malware protection systems. The dataset and all the coding files required for experiments reproducibility are publicly available at https://github.com/dcevallossalas/PrivacyMalwareClassifiers.

The inclusion of Internet of Things (IoT) for building smart cities, smart health, smart grids, and other smart concepts has driven data-driven decision making by managers and automation in each domain. However, the hyper-connectivity generated by IoT networks coupled with limited default security in IoT devices increases security risks that can jeopardize the operations of cities, hospitals, and organizations. Strengthening the security aspects of IoT devices prior to their use in different systems can contribute to minimize the attack surface. This study aimed to model a sequence of seven steps to minimize the attack surface by executing hardening processes. Conducted a systematic literature review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) techniques. In this way, we were able to define a proposed methodology to evaluate the security level of an IoT solution by means of a checklist that considers the security aspects in the three layers of the IoT architecture. A risk matrix adapted to IoT is established to evaluate the attack surface. Finally, a process of hardening and vulnerability analysis is proposed to reduce the attack surface and improve the security level of the IoT solution.