Background: The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, especially in underdeveloped countries. There is a clear need to develop novel computer-assisted diagnosis tools to provide rapid and cost-effective screening in places where massive traditional testing is not feasible. Lung ultrasound is a portable, easy to disinfect, low cost and non-invasive tool that can be used to identify lung diseases. Computer-assisted analysis of lung ultrasound imagery is a relatively recent approach that has shown great potential for diagnosing pulmonary conditions, being a viable alternative for screening and diagnosing COVID-19.Objective: To evaluate and compare the performance of deep-learning techniques for detecting COVID-19 infections from lung ultrasound imagery.Methods: We adapted different pre-trained deep learning architectures, including VGG19, InceptionV3, Xception, and ResNet50. We used the publicly available POCUS dataset comprising 3326 lung ultrasound frames of healthy, COVID-19, and pneumonia patients for training and fine-tuning. We conducted two experiments considering three classes (COVID-19, pneumonia, and healthy) and two classes (COVID-19 versus pneumonia and COVID-19 versus non-COVID-19) of predictive models. The obtained results were also compared with the POCOVID-net model. For performance evaluation, we calculated per-class classification metrics (Precision, Recall, and F1-score) and overall metrics (Accuracy, Balanced Accuracy, and Area Under the Receiver Operating Characteristic Curve). Lastly, we performed a statistical analysis of performance results using ANOVA and Friedman tests followed by post-hoc analysis using the Wilcoxon signed-rank test with the Holm’s step-down correction.Results: InceptionV3 network achieved the best average accuracy (89.1%), balanced accuracy (89.3%), and area under the receiver operating curve (97.1%) for COVID-19 detection from bacterial pneumonia and healthy lung ultrasound data. The ANOVA and Friedman tests found statistically significant performance differences between models for accuracy, balanced accuracy and area under the receiver operating curve. Post-hoc analysis showed statistically significant differences between the performance obtained with the InceptionV3-based model and POCOVID-net, VGG19-, and ResNet50-based models. No statistically significant differences were found in the performance obtained with InceptionV3- and Xception-based models.Conclusions: Deep learning techniques for computer-assisted analysis of lung ultrasound imagery provide a promising avenue for COVID-19 screening and diagnosis. Particularly, we found that the InceptionV3 network provides the most promising predictive results from all AI-based techniques evaluated in this work. InceptionV3- and Xception-based models can be used to further develop a viable computer-assisted screening tool for COVID-19 based on ultrasound imagery.

The 5G network was developed to push the capabilities of wireless networks to previously unseen performance limits, e.g., transmission rates of several gigabits per second, latency of less than a millisecond, and millions of devices connected at the same time. To meet these requirements, it is necessary to access new spectrum (the so-called millimeter waves) and use techniques such as Massive MIMO (Multiple-Input Multiple-Output) and beamforming. This required the design of a new radio interface, known as 5G NR, that includes improvements to its physical components and new protocols. The performance of the 5G network will depend heavily on the behavior of these new protocols under certain configuration parameters, traffic conditions, device density, and network architecture. This paper introduces an analytical model for the performance evaluation of 5G NR. The developed model describes the behavior of the different layer 1 and 2 protocols involved in 5G radio communication. Using the model, it is possible to evaluate the performance of 5G NR in terms of throughput and latency, two key performance metrics used to describe QoS (Quality of Service) thresholds of different applications. The protocol layer approach gives the model sufficient granularity to identify critical behaviors that significantly impact performance. This can help focus efforts on improving these key points or propose improvements/modifications to the operation of network protocols or devices. The use of this model for performance evaluation is exemplified by studying a Remote Driving scenario operated over 5G. This scenario has very stringent delay requirements, which, according to the model’s results, can be satisfied if the network conditions are adequate. This model and its results can be used as a starting point for performance evaluations of application involving end-to-end (E2E) communications.

Vehicular Ad Hoc Networks (VANETs) are considered by car manufacturers and the research community as the enabling technology to radically improve the safety, efficiency and comfort of everyday driving. However, before VANET technology can fulfill all its expected potential, several difficulties must be addressed. One key issue arising when working with VANETs is the complexity of the networking protocols compared to those used by traditional infrastructure networks. Therefore, proper design of the routing strategy becomes a main issue for the effective deployment of VANETs. In this paper, a reliable freestanding position-based routing algorithm (FPBR) for highway scenarios is proposed. For this scenario, several important issues such as the high mobility of vehicles and the propagation conditions may affect the performance of the routing strategy. These constraints have only been partially addressed in previous proposals. In contrast, the design approach used for developing FPBR considered the constraints imposed by a highway scenario and implements mechanisms to overcome them. FPBR performance is compared to one of the leading protocols for highway scenarios. Performance metrics show that FPBR yields similar results when considering freespace propagation conditions, and outperforms the leading protocol when considering a realistic highway path loss model.

Vehicular ad hoc networks have been identified as a key technology for enabling safety and infotainment applications in the context of smart and connected vehicles. In this sense, diverse approaches of multi-hop broadcast protocols have been proposed to collect and disseminate context information through the network. However, before vehicular ad hoc networks applications fulfill their expected potential to connect smart vehicles, several issues must be addressed. Among these issues, those related to security are of particular importance. In this article, the main security issues of broadcast message dissemination in vehicular ad hoc networks are discussed. Moreover, a review of the most relevant threats and proposed solutions to secure broadcast message dissemination in vehicular ad hoc networks is presented and discussed. As mentioned, security is an important topic which has not been fully addressed in vehicular ad hoc networks; therefore, the aim of this article is to introduce security issues and proposed solutions related to three main security concerns associated with the message dissemination process in vehicular ad hoc networks: network access, data consistency, and broadcast protocols.

Multi-unmanned aerial vehicle (multi-UAV) systems have become popular in applications such as precision agriculture, remote sensing, and pollution monitoring. Commonly, multi-UAV systems require to reach and maintain a specific flight formation during mission execution. This can be achieved by using a distributed UAV formation control strategy in which each UAV has a flight controller whose function is to calculate the control actions for the UAV actuators such that the UAV formation is maintained. To perform this task, the control strategy requires the reliable and timely exchange of information within the UAV formation. The information that is needed by the controller is commonly referred to as state information (SI). It has been assumed that SI can be properly disseminated by means of multi-hop communications, i.e., by deploying a flying ad-hoc network (FANET). In this sense, multi-hop broadcast protocols (MBPs) that were previously proposed for mobile and vehicular ad-hoc networks seem to be suited for this task. However, previous work dealing with distributed UAV formation control has made communication and networking assumptions that would be hard to fulfill in actual FANET deployments. Moreover, the efficiency of the MBPs to disseminate SI within a FANET remains unexplored. The goal of this paper is to analyze how the network performance offered by different MBPs impacts the effectiveness of distributed UAV formation control to maintain UAV formation. An evaluation framework to perform this task is proposed in this paper. The simulation results demonstrate the relevance of MBP performance in SI message dissemination and thus in the ability of the controller to maintain a formation.