Unmanned Aerial Vehicles (UAVs), as key enablers of 6G-enabled Internet of Things (IoT) ecosystems, facilitate dynamic aerial coverage, seamless edge intelligence, and adaptive routing. However, despite these advantages, the reliability and trustworthiness of UAV swarms in 6G remain critical concerns due to rising quantum threats and limitations of traditional machine learning approaches. The paper presents QuaRTA-6G, a Quantum-Resilient, Trust-aware, and Accountable framework that provides a unified solution for 6G UAV swarms. QuaRTA-6G achieves security, trust, and efficiency by seamlessly combining post-quantum cryptography for secure communication, a decentralized ledger for identity and trust management, and Variational Quantum Federated Learning (VQFL) for efficient swarm intelligence. For quantum-resistant authentication and immutable UAV identity verification, QuaRTA-6G leverages CRYSTALS-Kyber with blockchain. Through simulations, QuaRTA-6G achieves secure authentication handshakes in under 2 ms and scales robustly to swarms of more than 100 UAVs, keeping high integrity even under 20% packet loss. Experiments demonstrate that, even under strong data poisoning attacks, the framework achieves 92% accuracy and 85% mission success rate, while comprehensive resource analysis confirms its feasibility across both standard and resource-constrained UAV platforms. Furthermore, an ablation study demonstrates that each module of QuaRTA-6G is essential for ensuring a responsible and trustworthy 6G UAV framework.

Cancer patients are known to have a higher likelihood of developing Cardiovascular Disease (CVD) compared to non-cancer individuals. Although various types of cancer can contribute to the onset of CVD, lung cancer is inherently linked with increased susceptibility. To bridge this hypothesis, we propose a Lung cancer detection and Cardiovascular Disease Prediction (LCDP) system through lung Computed Tomography (CT) scan images. The lung cancer detection module of the LCDP system utilizes Transfer Learning (TL) with AdaDenseNet for classification. It employs the improvised Proximity-based Synthetic Minority Over-sampling Technique (Prox-SMOTE), improving accuracy. In the CVD prediction module, the feature extraction was performed using the VGG-16 model, followed by classification using a Support Vector Machine (SVM) classifier. The impact and interdependence of lung cancer on CVD were evident in our evaluation, with high accuracies of 98.28% for lung cancer detection and 91.62% for CVD prediction.

Transportation Cyber-Physical Systems (T-CPS) integrate transportation information with physical elements, enabling advanced features such as real-time vehicle tracking, collision avoidance, and intelligent traffic management. However, this also increases the need for improved security protocols to safeguard the critical identity-based data they transmit through vulnerable wireless networks, such as Vehicle Identification Numbers (VINs) and live Global Positioning System (GPS) coordinates. These T-CPS, traditionally protected by classical encryption algorithms, are now vulnerable due to the rise in quantum computing. Existing solutions are hindered by their complex security features and high computational overhead. To address these challenges, we propose Quantum Key Agreement and Signature Verification (QKASV), a post-quantum protocol that integrates Quantum Key Distribution (QKD) and Quantum Signatures for T-CPS. QKASV uses identity-tied QKD-based keys for session creation, followed by either individual or group quantum signatures. Signatures are created by applying basic quantum gates sequentially rather than lattice-based or certificate-based schemes. Hence, this speeds up the overall key generation, distribution, and management process without compromising the enhanced security. Formal security analysis using standard security models and the Scyther cryptographic protocol verification tool proves that QKASV meets standard security requirements. Further, comparison with similar schemes shows that QKASV operates on the least communication rounds required per signature, and reduces the computational overhead by at least 12%. Therefore, QKASV offers a better security solution compared to existing schemes.

The advent of 6G-enabled Internet of Everything(IoE) technologies is set to revolutionize urban infrastructures by providing fast, consistent, and low-delay capabilities for communication. 6G connectivity will integrate traffic and power grids for adaptive urban management. However, current traffic networks and power grids face critical challenges such as fragmented data processing, delayed responses, and outdated resource management leading to inefficiencies like traffic congestion and power outages. In 6G-enabled smart grid cities, system complexity and interdependence demand dynamic, real-time solutions, further exacerbating inefficiencies. To address these issues, this study introduces Digital Twin-enabled Real-time Optimization System (DT-ROS), a dynamic framework designed to optimize urban traffic and power grid systems. DT-ROS integrates a dual-tier Digital Twin (DT) and an advanced scheduling framework based on Priority Age of Information Deep Q Scheduler (PAoI-QS). The dual-tier framework builds an elementary and Integrated Digital Twin (IDT) with Auto-Regressive Integrated Moving Average (ARIMA)-based forecasting for accurate real-time traffic and energy demand predictions. The advanced scheduling framework minimizes the Age of Information (AoI), ensuring decision-making relies on the most current and relevant data. By continuously monitoring and processing real-time data, DT-ROS creates virtual models to simulate system behavior and dynamically allocate resources. Simulation results demonstrate the effectiveness of DT-ROS, achieving a 30% reduction in traffic congestion and a 25% improvement in power grid stability compared to existing methods. To create effective, robust, and sustainable urban systems for future smart cities, DT-ROS addresses traffic and electricity problems.

Autonomous Vehicles (AVs) are redefining the transportation sector through their ability to navigate, make decisions, and complete autonomous tasks. For accurate perception and comprehension of the surroundings, the AVs heavily rely on segmenting high-resolution 3D point cloud data provided by Light Detection and Ranging (LiDAR) sensors for discerning objects and other environmental features. However, the current vehicular segmentation approaches experience shortcomings in data insufficiency, computational performance, and precision concerns. Hence, to counteract these limitations, the paper proposes a Semantic Segmentation approach using Ball-Pivoting Algorithm and U-Net (SSBU) that harmoniously combines the Ball-Pivoting surface reconstruction algorithm and 3D U-Net to enhance image characteristics, leading to highly accurate outcomes with optimal cost efficiency. This SSBU integration involves carrying out augmentations and pre-processing of the raw LiDAR data to transform them into voxels through the process of Voxelization. The voxels are further improved through a surface reconstruction technique that utilizes the Ball Pivoting Algorithm (BPA). The resulting 3D model is analyzed using 3D U-Net deep learning architecture for robust and real-time interpretation. The implementation has produced a mean Intersection Over Union (IoU) of 83.3 over the NuScenes data and 69.7 on the KITTI dataset, outperforming the state-of-the-art.

The rapid improvement in 6G-enabled Autonomous Transport Systems (ATS) has enhanced operational efficiency in terms of communication speed, data processing, and vehicle coordination. However, it presents a critical challenge in enabling vehicles to handle unforeseen, real-time adverse conditions. Despite these advancements, the challenge of adapting to unpredictable traffic scenarios and operational anomalies persists, and there is still room for improvement in managing these situations without compromising decision-making or resource management. We propose the Distributed Intelligence Framework (DIF), which leverages Federated Learning (FL) and Digital Twins (DTs) to enhance decision-making and network resilience. FL enables collaborative learning among vehicles while ensuring sensitive data remains localized, and DTs simulate adverse traffic scenarios in real time, allowing proactive adjustments to resource allocation and traffic management. The DIF framework enables vehicles to learn from the experiences of others, allowing them to handle unique or adverse conditions that individual vehicles may not have encountered before. This collaborative approach strengthens the system’s ability to adapt to new challenges while safeguarding data integrity and ensuring operational efficiency. Experimental results show that DIF achieves a 65% reduction in convergence error within just five epochs, demonstrating significant improvements in both network resilience and decision-making, making it a critical advancement for the future of 6G-enabled ATS networks.

In autonomous vehicle navigation, the effectiveness and robustness of object detection models are directly influenced by the quality, quantity, and diversity of data. Detecting Vul-nerable Road Users (VRU) poses significant challenges due to their constant motion and dynamic behaviors. Utilizing real-time datasets like KITTI for training VRU detection models may not be optimal, as they lack coverage of adversarial situations and undervalue objects like pedestrians. Relying solely on such datasets for model training can lead to catastrophic real-world results. To address these issues, we present PointGAN, a framework supporting 3D object detection models that leverage conditional Generative Adversarial Networks (cGAN) to enhance dataset diversity specifically for the pedestrian class. PointGAN employs generative neural networks trained through multiple iterations to generate realistic point cloud data, guided by feedback from the discriminator until it closely mirrors real-world data. This strategy intends to significantly improve the overall performance of 3D object detection models by skillfully detecting pedestrians in challenging and diverse scenarios. The generative model achieves a Minimum Matching Distance - Earth Mover's Distance (MMD-EMD) score of 0.025, outperforming the existing state-of-the-art models trained under different categories.

The proliferation of Autonomous Vehicles (AVs) in recent times has opened up new possibilities for effective and secure transportation. However, with the increasing adoption of AVs, guaranteeing the accuracy and security of their sensory systems is becoming paramount. Specifically, the vulnerability of these systems to malware and sensor faults can pose significant risks to the dependable and secure operation of the vehicle. To identify and combat these issues we propose a stream-based Blockchain-powered Malicious Node Detection (BMND) method to analyze and report any malicious activity of the AV operating as a node on the Internet of Autonomous Vehicles (IoAV) network, wherein the existing solutions are at lower latency. BMND involves the detection of sensor anomalies and defects post-production of the AV. In the case that malware or any other malicious software is detected on the onboard compute unit it is isolated and contained, and the AV will be classified as malicious until appropriate remedial measures are taken to deter any sharing of erroneous or malicious data. When the AV is deemed safe from malware and defects in the system then a block is mined by the AV node and a unique ID is assigned to allow data transfers on the blockchain with other nodes. Only active nodes with assigned IDs and available blocks for transactions on the blockchain influence AV decision-making. BMND would allow modern AVs on the road to effectively communicate with reliable information. Experimental analysis shows that the malware detection in BMND is 4.4% more accurate with an F1-score of ~0.99 as compared to previous and other current state-of-the-art methods, and the communication capabilities of BMND are also better regarding security and latency concerning proposed vanilla blockchain methods.

Autonomous electric vehicles (AEVs) are revolutionizing the world of smart city transportation due to their low-resource consumption, improved traffic efficiency, zero carbon emissions, and improved road safety. To ensure the safe passage of vehicles through a complex environment, it is essential to plan for safe and smart navigation and energy management for AEVs. This demands an effective model for locating the optimal electric charging stations (ECSs) for scheduling and recharging the AEVs when they run on low battery. Many research works, however, do not focus on navigation and scheduling policies for AEV charging that would occur in extreme events in complex environments. This article puts forth a collaborative optimal navigation and charge planning (CONCP) framework based on multiagent deep reinforcement learning (MADRL). To ensure the safe passage of vehicles through the complex environment, it is essential to plan for safe and smart navigation and energy management for AEVs. The CONCP framework aims to achieve the best route from the origin to the final destination for each AEV, scheduling the optimal ECS while avoiding obstacles, reducing traffic congestion, and maximizing energy efficiency, accordingly. The experimental results indicate that CONCP achieves 27% higher success rates, 31% fewer collision rates, and 37% higher reward per episode than the other state-of-the-art algorithms.

The proliferation of fifth-generation (5G) networks toward vehicle-to-everything (V2X) communication has paved the way for driverless autonomous vehicles (AVs) in vehicular critical energy infrastructures (CEI). Though technological advancements improve AVs, the safety-critical messages (SCMs) still play a vital role in reducing crashes, preventing injuries, and saving lives. AVs’ high speed and complex network topology challenge disseminating SCMs with a highly successful delivery ratio and extremely low latency. Furthermore, the typical SCM dissemination schemes cause channel congestion and minimize the delivery ratio, making the systems incompatible with the AVs. Therefore, in this article, a software-defined-networking-assisted continuous clustering approach called migrating consignment region (MiCR) based on the federated$K$-means algorithm is proposed for disseminating SCMs to the AVs via 5G V2X communication. Unlike other methods that create clusters for every instance of SCM dissemination, MiCR continuously holds moving clusters for disseminating SCMs to AVs with ultrahigh reliability and low latency. The proposed MiCR approach has been simulated under real-time highway road maps and compared with other methods. The simulation results prove the superiority of MiCR in terms of network overload, SCM delivery ratio, latency, dissemination efficiency, and collision rate compared with the existing methods.