The wireless communication has played a significant impact on our daily lives introducing simplicity and making life more comfortable. As a result of faster technological advances in electronics and communications, the development of different types of unmanned aerial vehicles (UAVs) has become possible. Recently, many efforts have been made to develop more efficient inter-and intra-vehicle communication protocols introducing new challenges, e. g. multiple-UAV communication and Flying Ad Hoc Networks (FANETs). However, most of the experiments using real prototypes or systems are not feasible due to the costs and risks involved. Thus, simulating network protocol behavior in FANET scenarios is increasingly required to evaluate the applicability of developed network protocols. Thereby, we have been developing AVENS, a hybrid aerial network simulation framework, which merges LARISSA Architectural Model, X-Plane Flight Simulator and OMNeT++ Discrete Event Simulator. In a proof-of-concept study, we highlighted its advantages. Using AVENS, we can advance in the state-of-the-art concerning performance evaluation of intelligent aerial vehicles and provide means to evaluate the development of protocols, codes and systems more accurately.

Unmanned Aerial Vehicles (UAVs) demand technologies so they can not only fly autonomously, but also communicate with base stations, flight controllers, computers, devices, or even other UAVs. Still, UAVs usually operate within unlicensed spectrum bands, competing against the increasing number of mobile devices and other wireless networks. Combining UAVs with Cognitive Radio (CR) may increase their general communication performance, thus allowing them to execute missions where the conventional UAVs face limitations. CR provides a smart wireless communication which, instead of using a transmission frequency defined in the hardware, uses software transmission. CR smartly uses free transmission channels and/or chooses them according to application’s requirements. Moreover, CR is considered a key enabler for deploying technologies that require high connectivity, such as Smart Cities, 5G, Internet of Things (IoT), and the Internet of Flying Things (IoFT). This paper presents an overview on the field of CR for UAV communications and its state-of-the-art, testbed alternatives for real data experiments, as well as specifications to build a simple and low-cost testbed, and indicates key opportunities and future challenges in the field.

Different from traditional transport systems, such as cars or trains, which are limited by land transit space, flying cars (such as UAS, drones, and air taxis) do not occupy space with traffic. They have a degree of freedom in space and time, smaller displacement, and consequently, less stress for their users. Large companies and researchers around the world are working with different architectures, algorithms, and techniques to test air taxi transport to serve a significant proportion of people safely and autonomously. One of the main issues surrounding the diffusion of air taxis is safety and security, since a simple failure can lead to the loss of high-value assets, loss of the vehicle, and/or injuries to human lives, including fatalities. In this sense, despite significant efforts, the literature is still specific and limited regarding air taxi safety and security. Therefore, this study aimed to carry out an extensive systematic literature review of the main modern advances in techniques, architectures, and research carried out around the world focused on these types of vehicles. More than 210 articles from between 2015 and January 2022 were individually reviewed. In addition, this study also presents gaps that could serve as a direction for future research. As far as the authors are aware, no other study performs this type of review focused on air taxi safety.

FANETs (Flying Ad hoc Networks) are a new paradigm that can overcome mission constraints of single UAVs (Unmanned Aerial Vehicles). FANETs are composed of several UAVs that cooperate to accomplish some critical mission (i.e., hazardous area monitoring). In order to maintain UAVs coordination, all UAVs must continuously retransmit or relay messages through the wireless channel to assure that every UAV knows the FANET condition. However, when this message exchange is done arbitrarily, it may cause the broadcast storm problem, leading the wireless medium to an inoperable state. Despite some efforts reported in the literature for providing general broadcast storm problem mitigation techniques, the challenge of aggregating new information/knowledge to receivers, instead of just spreading the information in the network, has received less attention. In this paper, we not only prove that broadcast storm problem causes network contention as the number of UAVs increases, but also propose the innovative Dynamic Neighborhood-based Algorithm for the Broadcast Storm Problem (DNA-BSP), which was developed and validated based on outdoor experiments and computer simulations. It can mitigate the broadcast storm problem, which is a real challenge in FANETs, reducing message redundancy in more than 98%, and making message delivery more than 95% faster than in flooding scenario, outperforming classical broadcast storm mitigation techniques when applied in FANETs. Our detailed results can also guide future researches and provide useful insights for engineers planning and optimizing mission-critical mobile ad hoc network with support of UAVs.

Several market sectors are attracted by the potential of unmanned aerial vehicles (UAVs), such as delivery, agriculture, and cinema, among others. UAVs are becoming part of Internet of Things (IoT) networks in the development of autonomous and scalable solutions. However, these vehicles are gradually becoming attractive targets for cyberattacks. This study proposes the development of an efficient platform based on the Message Queuing Telemetry Transport (MQTT) protocol for UAV control and Denial-of-Service (DoS) detection embedded in the UAV system. For the efficiency test, latency, network and memory consumption on the platform were measured, in addition to the correlation between payload and delay time. The results of efficiency tests were collected for the three levels of quality of service (QoS). A strong correlation greater than 90% was found between delay and data size for all QoS levels, showing almost a linear proportion. In DoS detection, the best results were a true positive rate (TPR) of 0.97 with 16 features from the AWID2 dataset using LightGBM with Bayesian optimization and data balancing. Unlike other studies, the built platform shows efficiency for UAV control and guarantees security in the communication with the broker and in the Wi-Fi UAV network.