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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.

This paper presents and assesses various machine learning methods that aim at predicting path loss in rural environment. For this purpose, models such as artificial neural network (ANN), support vector regression (SVR), random forest (RF), and bagging with k -nearest neighbor (B- k NN) learners, are exploited and evaluated. They are trained and tested with path loss data collected from an extensive measurement campaign that have been carried out in diverse rural areas in Greece. The results demonstrate that all the proposed machine learning models outperform the empirical ones, exhibiting, in any case, root-mean-square-error (RMSE) values between 4.0 and 6.5 dB. The poorest prediction of the measured data is encountered for SVR with Polynomial kernel. Furthermore, B- k NN and RF algorithms preserve comparable path loss approximations with remarkably low RMSE on the order of 4.2–4.3 dB. The error metrics also reveal that increasing the number of hidden layers in ANNs, their performance is gradually enhanced. However, deeper layouts with more than three hidden layers do not markedly improve any further the prediction accuracy. Finally, the best prediction is achieved when employing a three-hidden layered ANN with 51 neurons evenly distributed among the layers. The specific layout exhibits the lowest RMSE value (4.0 dB), thus being highly recommended for accurate path loss predictions in rural locations.

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.

The cooperative aerial and device-to-device (D2D) networks employing non-orthogonal multiple access (NOMA) are expected to play an essential role in next-generation wireless networks. Moreover, machine learning (ML) techniques, such as artificial neural networks (ANN), can significantly enhance network performance and efficiency in fifth-generation (5G) wireless networks and beyond. This paper studies an ANN-based unmanned aerial vehicle (UAV) placement scheme to enhance an integrated UAV-D2D NOMA cooperative network.The proposed placement scheme selection (PSS) method for integrating the UAV into the cooperative network combines supervised and unsupervised ML techniques. Specifically, a supervised classification approach is employed utilizing a two-hidden layered ANN with 63 neurons evenly distributed among the layers. The output class of the ANN is utilized to determine the appropriate unsupervised learning method—either k-means or k-medoids—to be employed. This specific ANN layout has been observed to exhibit an accuracy of 94.12%, the highest accuracy among the ANN models evaluated, making it highly recommended for accurate PSS predictions in urban locations. Furthermore, the proposed cooperative scheme allows pairs of users to be simultaneously served through NOMA from the UAV, which acts as an aerial base station. At the same time, the D2D cooperative transmission for each NOMA pair is activated to improve the overall communication quality. Comparisons with conventional orthogonal multiple access (OMA) and alternative unsupervised machine-learning based-UAV-D2D NOMA cooperative networks show that significant sum rate and spectral efficiency gains can be harvested through the proposed method under varying D2D bandwidth allocations.

This paper presents an extensive path loss measurement campaign carried out in rural areas at 3.7 GHz, including line-of-sight (LOS) and non-LOS conditions. For this purpose, a commercially established fixed wireless access (FWA) network is exploited, operating with time-division long term evolution configuration. Furthermore, various models are examined and validated regarding their ability to predict accurately the path loss. The results reveal that the standard propagation model (SPM) achieves the best performance, thus being an attractive option for planning rural FWA links. The WINNER II and 3GPP/ITU-R models exhibit very good performance, as well. From the statistical assessment, the shadow fading follows the Lognormal distribution with a standard between 4.6 and 5.4 dB. An almost excellent fit is obtained regardless of the diverse propagation conditions in the specific area. Finally, from the model evaluation was concluded that SPM is highly recommended as the best option for a precise network dimensioning and planning.

Today’s terrestrial cellular communications networks face difficulties in serving coexisting users and devices due to the enormous demands of mass connectivity. Further, natural disasters and unexpected events lead to an unpredictable amount of data traffic, thus causing congestion to the network. In such cases, the addition of on-demand network entities, such as fixed or aerial base stations, has been proposed as a viable solution for managing high data traffic and offloading the existing terrestrial infrastructure. This paper presents an unmanned aerial vehicles (UAVs) aided offloading strategy of the terrestrial network, utilizing an unsupervised machine learning method for the best placement of UAVs in sites with high data traffic. The proposed scheme forms clusters of users located in the affected area using the k-medoid algorithm. Followingly, based on the number of available UAVs, a cluster selection scheme is employed to select the available UAVs that will be deployed to achieve maximum offloading in the system. Comparisons with traditional offloading strategies integrating terrestrial picocells and other UAV-aided schemes show that significant offloading, throughput, spectral efficiency, and sum rate gains can be harvested through the proposed method under a varying number of UAVs.

The employment of technology and digitization is crucial for cultural organizations to establish and sustain digital repositories for their cultural heritage artifacts. This exploitation is also essential in facilitating the presentation of cultural works and exhibits to a broader audience. Consequently, in this work, we propose a custom-developed digital repository that functions as software-as-a-service (SaaS), primarily promoting the safe storage, display, and sharing of cultural materials; enhancing accessibility; and fostering a deeper understanding and appreciation of cultural heritage. The proposed digital repository service is designed as a multitenant architecture, which enables organizations to expand their reach, enhance accessibility, foster collaboration, and ensure the preservation of their content. Moreover, our technology stack incorporates robust and reliable backend technologies, such as Django, to ensure data security and efficient management. Meanwhile, the frontend is powered by Angular, which guarantees a user-friendly and engaging interface for exploring and interacting with cultural materials. Specifically, this project aims to assist each cultural institution in organizing its digital cultural assets into collections and feeding other digital platforms, including educational, museum, pedagogical, and games, through appropriate interfaces. The creation of this digital repository offers a cutting-edge and effective open-access laboratory solution. It allows organizations to have a significant influence on their audiences by fostering cultural understanding and appreciation. Additionally, it facilitates the connection between different digital repositories and national/European aggregators, promoting collaboration and information sharing. By embracing this innovative solution, cultural institutions can benefit from shared resources and features, such as system updates, backup and recovery services, and data analytics tools, attributes that are currently provided by the platform.

One of the major challenges the mobile broadband community faces is the exponential increase in mobile data traffic, even more so, for cell-edge users. Thus, in a multitier network, the demand for high-speed and interference-free transmission and reception is inevitable. Beamforming (BF) is an advanced technology that offers a significantly improved solution to reduce the interference levels and improve the system capacity. Accordingly, the establishment of relays in mobile data networks has emerged spectral efficiency enhancements and cell capacity gains from an overall system perspective. This paper provides a comprehensive survey focused on the performance of adopted beamforming technique on MIMO relay networks that is expected to overcome crucial obstacles in terms of capacity and interference. The main objective is to point out the state-of-the-art research activity on BF techniques in MIMO relay networks, under various network performance challenges. Thereby, it focuses on recently developed procedures for interference modeling and mitigation, BF channel modeling, channel estimation and feedback, complexity and power consumption, adaptive BF for multiuser relaying, degrees of freedom, diversity issues, and spectral efficiency, in cooperative and opportunistic systems. Different network topologies have been considered and categorized, pertaining the challenges of BF implementation in MIMO relay networks.

In this study, a broadband Radio Frequency (RF) energy harvester implementation is presented. The system uses a broadband discone antenna, which can operate efficiently in a broad frequency spectrum, including LTE, DCS 1800 and UMTS 2100 cellular frequency bands. The system is able to harvest energy from various electromagnetic field sources, thus has the potential to efficiently charge a storage energy element in a short time. The prototype broadband RF energy harvester was tested in the laboratory and also in a typical urban environment.

This article presents the basic principles for an Extremely Low Frequency (ELF) IoT-based isotropic meter implementation, which can measure magnetic flux density from 100 nT up to 10 μT. The identical sensor probes are used for isotropic field measurements in the X, Y, and Z planes. The prototype has a flat response across the frequency range from 40 Hz to 10 kHz, detecting and measuring several magnetic field sources. The proposed low-cost meter can measure fields from the power supply network and its harmonic frequencies in the operating frequency band. The proposed magnetic flux density meter circuit is simple to implement and the measured field can be displayed on any mobile device with Wi-Fi connectivity. An Arduino board with the embedded Wi-Fi Nina module is responsible for data transferring from the sensor to the cloud as a complete IoT solution, supported by the Blynk application via Android and iOS operating systems or web interface. In addition, an ELF energy harvesting (EH) circuit was also proposed in our study for the utilization of the alternating magnetic fields (50 Hz) derived from the operation of several consumer devices such as transformers, power supplies, hair dryers, etc. using low-consumption applications. Experimental measurements showed that the (DC) harvesting voltage can reach up to 4.2 volts from the magnetic field of 33 μΤ, caused by the operation of an electric hair dryer and can fully charge the 100 μF storage capacitor (Cs) of the proposed EH system in about 3 min.