Mohamed I. Ibrahem

Flying Ad Hoc Networks (FANETs) composed of highly mobile Unmanned Aerial Vehicles (UAVs) are increasingly employed in mission-critical applications. However, the open wireless medium, high mobility, and resource constraints of UAVs expose FANETs to severe security and privacy threats, particularly at the Medium Access Control (MAC) layer. Existing authentication and key agreement schemes often suffer from high computational overhead and insufficient protection against advanced attacks. To address these challenges, this paper proposes a Lightweight Key Agreement Scheme (LWKAS) based on signcryption using Hyper Elliptic Curve Cryptography (HECC) and one-way cryptographic hash functions. The proposed scheme integrates MAC-layer security while simultaneously ensuring secure authentication, confidentiality, and location privacy through the Chinese Remainder Theorem (CRT). The security analysis demonstrates that LWKAS resists multiple attacks, including Man-In-The-Middle (MITM), replay, impersonation, Ephemeral Secret Leakage (ESL), cloning, desynchronization, and DoS attacks. The proposed scheme is implemented in NS-3 and evaluated against existing state-of-the-art methods in terms of authentication delay, communication overhead, and computational cost. The results demonstrate that LWKAS achieves significantly lower computational complexity and communication overhead while providing stronger security guarantees suitable for resource-constrained FANET environments.

Mobile Unmanned Aerial Vehicles (UAVs) forming Flying Ad Hoc Networks (FANETs) offer promising applications, but dynamic network structures, limited resources, and potential single points of failure create security challenges. While cluster-based data aggregation, where data is collected and combined at Cluster Heads (CHs) before transmission, improves efficiency, traditional techniques can compromise data privacy. This paper introduces SSDBFAN, a scalable and secure cluster-based data aggregation framework for Flying Ad Hoc Networks (FANETs). The proposed approach integrates the Frilled Lizard Optimization Algorithm (FLOA) for efficient cluster head selection with blockchain technology and post-quantum cryptographic techniques, including lattice-based homomorphic encryption and the Chinese Remainder Theorem, to ensure privacy-preserving data aggregation. Additionally, a hybrid online/offline signature mechanism is employed to achieve secure and efficient authentication with reduced computational overhead. The performance of the proposed framework is evaluated using NS-3 simulations under varying network sizes. Experimental results demonstrate that SSDBFAN significantly improves communication efficiency, reduces computational cost, and enhances network stability compared to existing schemes. Furthermore, scalability analysis with up to 500 UAV nodes confirms that the proposed framework effectively controls blockchain overhead, including bandwidth consumption, consensus latency, and storage requirements. Comparative evaluation with existing optimization algorithms shows that FLOA achieves superior performance in terms of cluster stability, delay, and throughput. These results validate the effectiveness of SSDBFAN as a scalable and security-aware solution for large-scale FANET environments.

Bilateral idiopathic carpal tunnel syndrome (CTS) is a neuromuscular condition involving the compression of the median nerve at both wrists, leading to pain, neurological symptoms, and loss of function. This paper proposes a robust machine-learning framework for a randomized crossover clinical trial comparing two physiotherapeutic treatment regimens: stretching followed by myofascial mobilization (S/M) and the reverse sequence (M/S). Instead of making inferences about the superiority of one treatment over another, the treatment regimen serves as a structured analytical label for investigating predictive separability, feature representation, and model stability within a controlled experimental setting. The clinical dataset of 73 patients underwent rigorous preprocessing, including strength feature aggregation and principal component analysis (PCA). Various classifiers were evaluated, with CatBoost achieving an ROC-AUC of 0.985 and a test accuracy of 96.5%, while Random Forest demonstrated strong adversarial robustness with an adversarial accuracy of 96.83%. To assess robustness, clinically constrained perturbations were introduced into the PCA feature space, simulating realistic input variability. The findings indicate that ensemble learning algorithms can capture structured patterns in crossover clinical datasets and remain stable under low-magnitude adversarial perturbations. The study underscores the importance of robustness evaluation and interpretability when applying machine learning models to biomedical data, particularly in small and well-structured clinical cohorts.

The use of web-enabled healthcare analytics has broadened access to machine learning (ML)- and AI-driven cloud models, but it has also created privacy and security challenges. Federated learning (FL) has been used to address data privacy issues; however, deployments of current FL architectures rely on centralized aggregation approaches, thereby creating a single point of failure (SPoF), as a successful adversarial attack on the global model during training or inference can compromise the entire system. These approaches also assume homogeneous data distributions across clients and overlook the constraints and diversity of web-based analytics. To address those limitations, traditional blockchain-based FL systems incorporated distributed ledgers to record model updates and artifacts. However, using the chain as a data ledger to record model artifacts and logs increases consensus overhead and coordination costs. This paper introduces Blockchain-based Clustered Federated Learning (BCFL), an architecture-diverse and cluster-based FL framework. Our approach is coordinated by a lightweight permissioned ledger that eliminates the trusted central aggregator while preserving utility, robustness, and verifiable provenance in web-based healthcare analytics. BCFL records compact provenance metadata on-chain while keeping model parameters off-chain. In addition, by distributing trust across clusters, the design reduces the transfer of adversarial attacks across models by limiting the impact of malicious updates during training and improving reliability at inference time. Experiments on real-world healthcare data and other benchmarks show that BCFL improves the performance of trained AI/ML models and reduces attack success rates compared with several FL baselines.

The evolution toward 6G wireless networks demands ultradense cell-free massive multiple-input multiple-output (MIMO) systems that can deliver unprecedented connectivity while preserving data privacy and enabling rapid adaptation to dynamic conditions. Current resource allocation approaches rely on centralized deep reinforcement learning (C-DRL) frameworks that create scalability bottlenecks, require extensive training periods, and violate emerging privacy regulations through global data aggregation. This article introduces a privacy-preserving federated meta-learning (PP-FML) framework that addresses these fundamental limitations through distributed intelligence and instant adaptation mechanisms. The proposed approach enables each access point (AP) to learn optimal resource allocation policies locally while collaboratively improving system-wide performance through cryptographically secure gradient sharing. The meta-learning component provides few-shot adaptation capabilities, allowing networks to respond to new conditions within minutes rather than hours. Comprehensive performance evaluation demonstrates that PP-FML achieves 42.3% sum rate improvement, 28.8% better energy efficiency (15.2 bits/Hz/J), subminute adaptation latency (51 s), and strong privacy guarantees epsilon 1.0 differential privacy compared to centralized approaches while maintaining complete data privacy and enabling rapid adaptation to changing network conditions. The framework scales linearly to ultradense deployments exceeding 300 APs per square kilometer with constant per-node computational complexity, making it suitable for practical 6G network deployment with heterogeneous device populations, including mobile users and diverse applications.

The rapid evolution of beyond fifth-generation (B5G) and sixth-generation (6G) networks is reshaping mobile edge computing (MEC) to support large-scale, heterogeneous Internet of Things (IoT) deployments and complex cyber-physical systems (CPSs). Conventional data-driven intelligence in MEC traditionally relies on centralized learning paradigms that often fail to meet the privacy, latency, scalability, and adaptability requirements in distributed and resource-constrained environments. To address these shortcomings, the objective of our work in this article is to investigate the paradigm shift toward distributed learning and demonstrate how its co-design with emerging communication and system-level technologies can enable scalable and trustworthy intelligence for next-generation IoT and CPS. Building on this objective, we conduct a systematic survey of recent studies and analyze 12 key enabling technologies, including concept drift adaptation, transformers, TinyML, blockchain, integrated sensing and communication (ISAC), digital twins, explainable AI (XAI), federated learning (FL) and unlearning, adversarial ML (AdvML), meta-learning, and multiarmed bandits (MABs). The surveyed literature is organized using a unified taxonomy and an integrated conceptual pipeline, which clarifies how these enablers interact across sensing, communication, computation, trust, and adaptation layers of IoT and CPSs. The main outcomes of this study include: 1) a comprehensive taxonomy characterizing enabling technologies for distributed edge intelligence; 2) a comparative synthesis of representative works highlighting common architectural patterns and evaluation practices; and 3) the identification of research gaps, critical tradeoffs, and open challenges, particularly related to model robustness, energy efficiency, data heterogeneity, and secure real-time inference. Overall, this survey establishes a structured foundation and forward-looking roadmap for designing scalable, privacy-preserving, and intelligent distributed learning systems in future B5G- and 6G-enabled IoT and CPS environments.

Recently, bandpass surface acoustic wave (SAW) filter-based wavelet transforms have become a more efficient tool in most communication systems. Compared to passive filters, these filters have linearity in the phase response, small size, and simplicity in design. Additionally, the wavelet algorithm offers good localization in the double-dispersive channel. However, one limitation of these filters is the presence of passband fluctuations commonly known as insertion loss and elevated sidelobe power, which negatively impact the filter’s frequency response. This paper proposes a novel bandpass filter-based wavelet transform scheme using SAW design. The main lobe of a regular SAW filter has been concentrated through a couple of stages to construct the design. This design achieves an Insertion loss of 6.488 dB, and the first side lobe reduction reaches 151.7 dB. Additionally, the simulation incorporates various substrate materials and analyzes how they respond to our design. The simulation includes the center frequency of the very high frequency (VHF) range on a different scale. A comparison of different materials and frequencies is measured and simulated in both time and frequency domains.