Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.

Relays receive and retransmit signals between one or more sources and one or more destinations. Cooperative relaying is a novel technique for wireless communications that increases throughput and extends the coverage of networks. The task of relay selection serves as a building block to realize cooperative relaying. Recently, relays with buffers have been incorporated into cooperative relaying providing extra degrees of freedom in selection, thus improving various performance metrics, such as outage probability, power reduction, and throughput, at the expense of tolerating an increase in packet delay. In this survey, we review and classify various buffer-aided relay selection policies and discuss their importance through applications. The classification is mainly based on the following aspects: 1) duplexing capabilities, 2) channel state information (CSI), 3) transmission strategies, 4) relay mode, and 5) performance metrics. Relay selection policies for enhanced physical-layer security and cognitive communications with reduced interference are also discussed. Then, a framework for modeling such algorithms is presented based on Markov Chain theory. In addition, performance evaluation is conducted for various buffer-aided relay selection algorithms. To provide a broad perspective on the role of buffer-aided relay selection, various issues relevant to fifth-generation (5G) networks are discussed. Finally, we draw conclusion and discuss current challenges, possible future directions, and emerging technologies.

Educational learning spaces encompass a spectrum, from traditional classrooms to contemporary online platforms and immersive virtual reality settings, fostering versatile teaching methods like collaboration, project-based, and experiential learning. Our study delves into modern higher education environments, particularly the shift from conventional to innovative spaces. Our focus centers on the benefits and challenges intrinsic to these emerging learning spaces, intending to enlighten educators, policymakers, and researchers. Our exploration commences by revealing the limitations entrenched within traditional learning environments, emphasizing the vital need for inventive solutions to meet evolving educational demands. We further investigate diverse learning scenarios, ranging from hybrid and remote setups to the integration of online platforms and virtual tools. Through this lens we navigate complexities introduced by these novel modalities, including potential reductions in face-to-face interactions and heightened demand for adept instructional and technological support. Lastly, our inquiry underscores the disparities between traditional and contemporary learning spaces, accentuating the potential for innovative settings to elevate higher education quality. Here, we illuminate the anticipated merits of such spaces, notably heightened student engagement, enriched collaboration, and amplified creativity. Concurrently, we explore technology’s pivotal role in shaping learning environments and ultimately influencing pedagogical methodologies. Our future research will explore how Artificial Intelligence can improve higher education.

As the Internet of Things (IoT) ecosystem evolves, innovative applications with stringent demands with respect to latency will emerge. To handle computation-intensive tasks in a timely manner, data offloading to Mobile Edge Computing (MEC) servers has been suggested. On the other hand, prospective IoT networks are expected to include Unmanned Aerial Vehicles (UAVs) to enhance coverage and connectivity, while retaining reliable communication links with ground nodes in urban, suburban, and rural terrain. Nevertheless, the evolution of UAV-aided MEC-enabled IoT presupposes the mitigation of security threats through the implementation of efficient and robust countermeasures. As UAVs inherently have certain limitations in terms of energy, computational, and memory resources, designing lightweight security solutions is required. This paper provides an overview of the UAV-aided MEC-enabled IoT and a detailed presentation of use cases and application scenarios, where security is of utmost importance. Subsequently, up-to-date research works on security solutions for the UAV-aided MEC-enabled IoT are comprehensively presented. To this end, the adoption of information-theoretic techniques that ensure adequate Physical-Layer Security (PLS) is discussed along with sophisticated security approaches based on emerging technologies, such as Blockchain and Machine Learning (ML). In addition, research studies on software- and hardware-based methods for the identification and authentication of network nodes are presented. Finally, this paper provides future perspectives in this research domain, stimulating further work.

During the last few years, a wide variety of Internet of Drones (IoD) applications have emerged with numerous heterogeneous aerial and ground network elements interconnected and equipped with advanced sensors, computation resources, and communication units. The evolution of IoD networks presupposes the mitigation of several security and privacy threats. Thus, robust authentication protocols should be implemented in order to attain secure operation within the IoD. However, owing to the inherent features of the IoD and the limitations of Unmanned Aerial Vehicles (UAVs) in terms of energy, computational, and memory resources, designing efficient and lightweight authentication solutions is a non-trivial and complicated process. Recently, the development of authentication mechanisms for the IoD has received unprecedented attention. In this paper, up-to-date research studies on authentication mechanisms for IoD networks are presented. To this end, the adoption of conventional technologies and methods, such as the widely used hash functions, Public Key Infrastructure (PKI), and Elliptic-Curve Cryptography (ECC), is discussed along with emerging technologies, including Mobile Edge Computing (MEC), Machine Learning (ML), and Blockchain. Additionally, this paper provides a review of effective hardware-based solutions for the identification and authentication of network nodes within the IoD that are based on Trusted Platform Modules (TPMs), Hardware Security Modules (HSMs), and Physically Unclonable Functions (PUFs). Finally, future directions in these relevant research topics are given, stimulating further work.