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.

Over the coming years, it is expected that the number of machine-to-machine (M2M) devices that communicate through LTE-A networks will rise significantly for providing ubiquitous information and services. However, LTE-A was devised to handle human-to-human traffic, and its current design is not capable of handling massive M2M communications. Access Class Barring (ACB) is a congestion control scheme included in the LTE-A standard that aims to spread the accesses of user equipments (UEs) through time so that the signaling capabilities of the eNB are not exceeded. Notwithstanding its relevance, the potential benefits of the implementation of ACB are rarely analyzed accurately. In this paper, we conduct a thorough performance analysis of the LTE-A random access channel (RACH) and ACB as defined in 3GPP specifications. Specifically, we seek to enhance the performance of LTE-A in massive M2M scenarios by modifying certain configuration parameters and by the implementation of ACB.We observed that ACB is appropriate for handling sporadic periods of congestion. Concretely, our results reflect that the access success probability of M2M UEs in the most extreme test scenario suggested by the 3GPP improves from approximately 30%, without any congestion control scheme, to 100% by implementing ACB and setting its configuration parameters properly.

In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to be recognized and extracted by experts for classification of tumors into different tumor types, as well as segmentation of tumor images. This article summarizes the latest studies of deep learning techniques applied to three different kinds of brain cancer medical images (histology, magnetic resonance, and computed tomography) and highlights current challenges in the field for the broader applicability of DCNN in personalized brain cancer care by focusing on two main applications of DCNNs: classification and segmentation of brain cancer tumors images.

The accelerating complexity of urban environments has prompted cities to adopt digital technologies that improve efficiency, sustainability, and resilience. Among these, Urban Digital Twins (UDTw) have emerged as transformative tools for real-time representation, simulation, and management of urban systems. This Systematic Literature Review (SLR) examines the integration of Digital Twins (DTw), the Internet of Things (IoT), and Artificial Intelligence (AI) into the Smart City Development (SCD). Following the PSALSAR framework and PRISMA 2020 guidelines, 64 peer-reviewed articles from IEEE Xplore, Association for Computing Machinery (ACM), Scopus, and Web of Science (WoS) digital libraries were analyzed by using bibliometric and thematic methods via the Bibliometrix package in R. The review allowed identifying key technological trends, such as edge–cloud, architectures, 3D immersive visualization, Generative AI (GenAI), and blockchain, and classifies UDTw applications into five domains: traffic management, urban planning, environmental monitoring, energy systems, and public services. Persistent challenges have been also outlined, including semantic interoperability, predictive modeling, data privacy, and impact evaluation. This study synthesizes the current state of the field, by clearly identifying a thematic mapping, and proposes a research agenda to align technical innovation with measurable urban outcomes, offering strategic insights for researchers, policymakers, and planners.

Using cellular networks for providing machine-to- machine (M2M) connectivity offers numerous advantages regarding coverage, deployment costs, security and management, among others. Nevertheless, having a large number of M2M devices activated simultaneously is difficult to tackle at the evolved Node B, and it causes complications in the connection establishment. The random access channel (RACH) in LTE-A is adequate for handling human-to-human (H2H) communications. However, for the efficient provision of simultaneous H2H/M2M communications, it is necessary to optimize the available access control mechanisms so that network overload is avoided and a better QoS can be offered. Access Class Barring (ACB) has shown to be effective in reducing the number of simultaneous users contending for access. However, it is still not clear how to dynamically adapt its parameters, especially in highly changing scenarios with bursty traffic as it can occur when M2M communications are involved. We propose a dynamic algorithm based on reinforcement learning to adapt the barring rate parameter of ACB. This algorithm can adapt it to different traffic conditions, reducing congestion and hence the number of collisions in the RACH. The results show that our proposed mechanism increases the access success probability for all the users while barely impacting H2H users and other key performance indicators.

The performance evaluation of the random access (RA) in LTE-A has recently become a major research topic as these networks are expected to play a major role in future 5G networks. Up to now, the key performance indicators (KPIs) of the RA in LTE-A have been obtained either by performing a large number of simulations or by means of analytic models that, oftentimes, sacrifice precision in exchange for simplicity. In this paper, we present an analytical model for the performance evaluation of the LTE-A RA procedure that incorporates the access class barring (ACB) scheme. By means of this model, each and every one of the KPIs suggested by the 3GPP can be obtained with minimal error when compared with results obtained by simulation. To the best of our knowledge, this paper presents the most accurate analytical model, which can be easily adapted to incorporate modifications of network parameters and/or extensions to the LTE-A system. In addition, our model of the ACB scheme can be easily incorporated to other analytic models of similar nature without further modifications.