Demosthenes Vouyioukas

This paper investigates a security-aware design for unmanned aerial vehicle (UAV)-assisted communication networks in adversarial environments with multiple aerial eavesdroppers (AEs) whose locations are imperfectly known. To enhance physical layer security (PLS), multiple cooperative aerial jammers (AJs) transmit artificial noise (AN), aiming to suppress the interception capability of AEs. Imperfect channel state information (CSI) is modeled via bounded small-scale fading errors, and location uncertainty of AEs is characterized by bounded spherical regions, enabling a deterministic worst-case secrecy rate formulation. Based on these uncertainty models, a worst-case robust secrecy energy efficiency (SEE) metric is defined as the ratio between the aggregate worst-case secrecy rate and the total energy consumption. Given the non-convex nature of this problem, a tractable solution framework is developed based on Dinkelbach’s method, block coordinate descent (BCD), and successive convex approximation (SCA). Simulation results reveal the benefits of cooperative multi-AJ operation in enhancing secrecy robustness against CSI errors and location uncertainty of AEs while maintaining energy efficiency.

This paper introduces a coordinated framework in which a base station (BS) and a Reconfigurable Intelligent Surfaces (RIS) jointly support their respective user groups while mitigating the impact of a Uncrewed Aerial Vehicle (UAV)-based jammer. Specifically, the RIS-assisted users rely on a reflective surface under attack from an UAV-jammer, and the BS simultaneously supports its own associated UEs alongside the RIS. We adopt an alternating optimization strategy that decomposes the original non-convex formulation into tractable subproblems using Block Coordinate Descent (BCD), thereby enabling multi-tier cooperation in complex interference environments. We frame our objective as the sum rate maximization, a metric for overall system performance, to maintain service quality and enhance robustness under severe jamming. The core of our methodology is the transformation of the resulting non-convex subproblems into tractable convex formulations via Semidefinite Programming (SDP). We employ a joint design of beamforming and phase shift optimization, whose combined effect leads to substantial performance improvements within the proposed framework. Extensive simulations and comparisons with existing algorithms demonstrate that this design achieves superior interference suppression and resource efficiency, significantly outperforming state-of-the-art baselines in sum rate, energy efficiency, UAV-jamming resilience and group-aware support for both BS- and RIS-served users.

This letter presents a preliminary statistical characterization of the in-cabin channel at multiple frequencies. Narrowband measurements are launched inside a medium-size and range Airbus A321-200 aircraft for frequencies between 3.5 and 60 GHz. According to the results Alpha Beta Gamma (ABG) model predicts the multi-frequency path loss with substantial efficacy against other examined empirical models. The shadow fading increases progressively with frequency with a standard deviation from 1.9 up to 3.0 dB. The small-scale fading exhibits Rayleigh characteristics with negative K-factors at lower frequencies (3.5 and 8 GHz), which gradually increase and become positive at higher frequencies (14 and 60 GHz), indicating a Rician fading channel. The spatial correlation of the channel is modeled using an exponentially decaying sinusoidal pattern, with decorrelation occurring more rapidly at higher frequencies. Finally, the channel is found to be favorable for the application of frequency diversity.

In this paper, we introduce an innovative artificial intelligence (AI)-driven methodology for optimizing the deployment of unmanned aerial vehicle (UAV)-enabled networks, specifically addressing scenarios where an untrusted UAV serves as an amplify-and-forward (AF) relay while potentially engaging in eavesdropping activities. The primary goal is to enhance secrecy performance by optimizing both the three-dimensional (3-D) positioning of the UAV relay and the power allocation strategy. Conventional optimization methods often fail to effectively capture the intricate, dynamic nature of such networks, thereby limiting their ability to ensure adaptive secrecy optimization. To address this challenge, we propose a hybrid approach that integrates particle swarm optimization (PSO) with the K-means and K-medoids unsupervised machine learning (ML) techniques. This combined approach not only refines UAV placement but also ensures adherence to the derived rule-based power allocation conditions, which are crucial for maximizing secrecy performance. Through extensive simulations, we evaluate the efficacy of the proposed K-PSO methodology against standalone PSO, K-means, and K-medoids models. The results demonstrate that the hybrid K-PSO approach consistently outperforms its standalone counterparts by significantly improving the average achievable secrecy rate while reducing the secrecy outage probability (SOP).

During the last few years, Unmanned Aerial Vehicles (UAVs) have increasingly become primary components of various critical civilian and military applications. As technology rapidly evolves, particularly in the realm of Software-Defined Radio (SDR) and Field-Programmable Gate Arrays (FPGAs), advanced communication protocols and signal processing methods are expected to emerge within UAV-based systems. Crucially, UAVs are expected to capitalize on SDR to enhance communication, sensing, data processing, and defense mechanisms. With this perspective in mind, this paper provides a comprehensive up-to-date review of the integration of SDR technology in UAV-based systems, encompassing the latest techniques, methodologies, and challenges. Specifically, this paper examines case studies and real-world implementations of SDR-assisted UAV-based systems across various domains, including communication, security, detection, classification, and localization, elucidating their efficacy, constraints, and areas for potential improvement. Through this review, valuable insights are offered to researchers, engineers, and practitioners interested in harnessing the synergies between SDR and UAV technologies to address the evolving requirements of contemporary applications and pave the path for future innovations in the field.

In the ever-evolving landscape of wireless communication systems, including fifth-generation (5G) networks and beyond (B5G), accurate Modulation and Coding Scheme (MCS) prediction is crucial for optimizing data transmission efficiency and quality of service. Traditional MCS selection methods rely on predefined rules and heuristics, offering transparency and control but lacking adaptability in changing wireless conditions. The emergence of Machine Learning (ML) has brought transformative capabilities, particularly in MCS prediction. ML leverages data-driven models, promising improved accuracy and adaptability in dynamic wireless environments. This paper marks a novel endeavor in this domain, as it explores and evaluates a range of machine learning (ML) techniques for predicting MCS in orthogonal frequency-division multiplexing (OFDM) systems, representing the first such investigation in this field. Additionally, it introduces a specialized Deep Neural Network (DNN) architecture with two hidden layers for MCS prediction, guided by performance metrics such as accuracy, precision, recall, and F1-score. The examined ML methods include Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest (RF), and Bagging with k-NN (B-kNN). These methods undergo thorough training and evaluation using a dataset generated from simulations of non-standalone 5G networks. The study incorporates physical layer measurements and employs a ray-tracing path loss prediction model for comprehensive environmental characterization. Also, advanced data mining techniques preprocess raw data, addressing model underfitting and overfitting challenges. Finally, performance evaluation results reveal that the ANN with two hidden layers achieves the highest accuracy at 98.71%, while RF and B-kNN methods attain the lowest accuracy, below 88.65%. SVM and ANN models, with one and four hidden layers, respectively, demonstrate comparable MCS prediction accuracy, ranging from 97.02 to 97.30%.

This paper proposes a security-aware computation offloading framework tailored for mobile edge computing (MEC)-enabled Internet of Things (IoT) networks operating in environments with aerial eavesdroppers (AEs) and ground eavesdroppers (GEs). It is envisaged that multiple ground nodes (GNs) should perform computation tasks partly locally and partly remotely by offloading a portion of these tasks to MEC servers. To facilitate this paradigm, an unmanned aerial vehicle (UAV) is deployed, serving as both an aerial MEC server and a relay for forwarding part of the tasks to a ground access point (AP) for computing. The computation offloading is further reinforced by incorporating a reconfigurable intelligent surface (RIS) unit in close proximity to the AP. Within this context, this paper provides an analysis of the secrecy outage probability (SOP) and formulates an optimization problem aimed at maximizing the minimum secure computation efficiency (SCE) by jointly optimizing transmit power allocation, time slot scheduling, task allocation, and RIS’s phase shifts. Given the non-convex nature of the problem, an iterative algorithm is introduced to address the fractional objective function and coupled optimization variables by employing Dinkelbach- and block coordinate descent (BCD)-based methods, respectively. The obtained results confirm the efficacy of the optimized scheme.

The development of a large-scale widespread spectrum monitoring network (SMN) is essential for spectrum management and interference identification at a national level. The financial outlay involved in setting up such a system is considerable, encompassing the acquisition of requisite equipment and installation infrastructure. However, the expenses can be significantly curtailed by leveraging the existing infrastructure of other service providers, such as mobile network operators (MNOs), via collocation. Nevertheless, this strategy requires the selection of suitable sites among the available ones to develop an SMN that is both efficient and cost-effective. Toward this end, this article presents the methodological framework for designing a nationwide SMN by collocating with existing MNOs’ infrastructure. The proposed approach is evaluated using computer simulations in the context of the spectrum monitoring requirements in Greece. Specifically, the network’s performance is assessed according to International Telecommunication Union (ITU) guidelines and the spectrum limitations of Greece’s national regulatory authority (NRA). The results demonstrate that a network based on time difference of arrival (TDoA) with 160 sensors can adequately cover the entire region, including the mainland, islands, and surrounding seas. Adding angle-of-arrival (AoA) capabilities to 30 out of 160 sites enables the hybrid geolocation technology and improves the geolocation ability and accuracy of the SMN network noticeably.

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 (C_s) of the proposed EH system in about 3 min.