Beyond 5G (B5G) networks must support diverse services such as enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). Static scheduling fails under such heterogeneity, leading to poor spectral efficiency and degraded QoS. We present TRIP-B5G, a unified framework combining a lightweight Support Vector Machine (SVM) traffic classifier and a Proximal Policy Optimisation (PPO) agent for slice-aware physical resource block (PRB) allocation in Open Radio Access Networks (O-RAN). Using Key Performance Indices (KPI)-driven features (e.g., Channel Quality Indicator, buffer occupancy, throughput), the SVM classifies traffic slices, while PPO dynamically allocates PRBs with slice-specific reward functions. Implemented on srsRAN, Open5GS, and O-RAN Near-Rt RIC, TRIP-B5G achieves up to 25% higher eMBB throughput, 28% lower URLLC latency, and 15% higher mMTC access success, reclaiming more than 90% of idle PRBs. Results confirm that KPI-driven ML and RL co-design enables practical AI-native RAN control in B5G.

In disaster-affected areas where conventional communication infrastructure collapses, Unmanned Aerial Vehicles (UAVs) can act as mobile IoT base stations to restore connectivity. However, maintaining reliable Low Power Wide Area links such as LoRa remains challenging due to terrain-dependent propagation, interference, and energy constraints. This work introduces RescueNav, an intent-driven UAV trajectory planning framework that integrates LoRa-specific signal constraints with reinforcement-learning-based decision-making. Unlike existing methods that treat communication as a secondary factor, RescueNav formalizes the operator’s intent to maintain Line of Sight connectivity, maximize coverage, and conserve UAV energy directly into the trajectory optimization process. By leveraging Q Learning with Prioritized Experience Replay, RescueNav dynamically adapts flight paths to evolving disaster environments, guided by a shaped reward function that encodes communication and energy objectives. Experiments with a Holybro S500 V2 platform equipped with a Pixhawk, Raspberry Pi, and LoRa modules demonstrate that RescueNav improves median signal strength by 4×, enhances throughput by 42%, and achieves a 55% higher cumulative reward than standard Q Learning. These results highlight the potential of intent-based reinforcement learning for resilient IoT communication in mission-critical post-disaster scenarios.

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.

Rapid advancement of radar technology has led to the emergence of cognitive radar systems, which utilize adaptive mechanisms to optimize performance in dynamic environments. This paper explores the integration of cognitive adaptability into smartphone-based Impulse Radio Ultra-Wideband (IRUWB) radar systems. By dynamically modifying the radar’s operational parameters based on real-time output analysis, we aim to address the limitations of current smartphone radar implementations, including high power consumption, static radar configurations, and the inherent mobility of smartphones. Our proposed Cognitive-Adaptive IR-UWB Radar (CAIR) system improves accuracy, power efficiency, and responsiveness, enabling effective target detection, target classification, gesture recognition, distance estimation, and vital sign monitoring in diverse scenarios. By incorporating cognitive radar principles, we present a novel approach to overcoming the challenges of varying environmental conditions and user contexts, ultimately delivering a more robust and versatile user experience. This paper outlines the CAIR architecture, algorithmic design, and adaptive control mechanisms, showcasing its potential to enhance smartphone radar sensing. When tested against the major smartphone use cases, our system improves accuracy by up to 11.5%, while achieving cognitive adaptability of up to 90%. Additionally, the Artificial Neural Network (ANN)-based cognitive model achieves an accuracy of 95% and an F1-score of 94%.

Connected Autonomous Vehicles (CAVs) are poised to revolutionize intelligent transportation by enhancing road safety and reducing traffic congestion through real-time communication. However, this dependency introduces vulnerabilities to cyberattacks, especially in emergency scenarios where secure, low-latency communication is critical. While Quantum Key Distribution (QKD) provides quantum-secure key exchange, relying solely on it for all communication processes can introduce latency during key generation, which can be problematic in time-sensitive situations. To address this, we propose HyQCAN, a Hybrid Quantum-Classical Authentication Network that integrates QKD with Dynamic Basis Switching (DBS) for vehicle identity authentication of emergency vehicles in Vehicular Ad Hoc Networks (VANETs). QKD ensures quantum-secure encryption, while DBS enables real-time adaptation of the key exchange process based on communication urgency. HyQCAN achieves an optimal balance between security and responsiveness, with an Average Authentication Time (AAT) of 3.34 ms and an Average Key Generation Efficiency (AKGE) of 111.82 bps, effectively safeguarding critical VANET communications in high-priority situations.

This paper introduces the Next-Generation Mobile Hotspot (NGMHS), a breakthrough solution for optimizing 5G mobile hotspot performance through intelligent, AI-powered traffic management and user-focused controls. Leveraging extended Berkeley Packet Filter (eBPF) technology, NGMHS efficiently monitors per-client traffic, reducing processing overhead while providing precise real-time control. Central to NGMHS is the AI-based Network Service Detector (NSD+), which dynamically prioritizes real-time traffic, significantly enhancing video call bitrates and reducing latency for gaming applications. Our evaluations on the Samsung A54 and Galaxy S24 devices demonstrate marked improvements in user experience, with innovations such as privacy-focused OTP user-profiles and granular traffic insights. NGMHS represents a pioneering step forward in 5G connectivity, offering a seamless, secure, and highly customizable mobile hotspot experience. NGMHS is successfully deployed across Samsung's A, M, S, Fold, and Flip series models, where increased user engagement and consistent performance improvements have been observed post-deployment.

Autonomous vehicle (AV) usage has become predominant in the rapidly evolving landscape of urban transportation. Integrating AVs and non-AVs in the existing traffic infrastructure has significantly increased the complexity of traffic patterns. This research work primes the enhanced Traffic Management System (e-TMS), a solution implemented to expedite emergency vehicle (EV) travel in the context of connected AVs (CAVs) and ensured secured communication employing public key infrastructure (PKI) among the Internet of Vehicles (IoV) framework. When an EV is detected, the IoV system verifies the EV’s signal using PKI, ensuring its authenticity and integrity by encrypting the communication between the EV and road side units (RSUs). Further, the e-TMS activates the platooning process where CAVs in the EV’s lane shift to the adjacent lanes and dynamically form platoons to create a dedicated lane for the EV. To further optimize the platooning process, an adaptive smart leader selection (ASLS) algorithm is employed to select a leader vehicle spontaneously among the AVs based on the proximity to the EV, communication reliability, and lane position. Radio Detection and Ranging devices aid this platooning process by providing distance and target velocity, which are needed to maintain the required distance between the AVs and ensure safe platoon formation. The e-TMS enhances EV response times and overall traffic flow efficiency and resulted in 27.8% increase in mean speed compared to the traditional methods.

Establishing reliable communication networks in post-disaster environments is essential for effective emergency response. Deploying Unmanned Aerial Vehicles (UAVs) equipped with base stations provides a rapid and promising solution for restoring connectivity. However, onboard path planning is computationally expensive due to the constantly varying terrain, and precomputing paths for all locations is impractical. We propose WAPPOS (Waypoint Assisted Path Planning for Off-board Systems), a data and knowledge-driven framework that optimizes UAV path planning through distributed off-board processing. WAPPOS integrates satellite imagery from Google Earth into its Target Region Mapping module, which employs the DeepLabV3+ model to segment buildings into No-Fly Zones (NFZs) and Fly Zones (FZs) with 92.2% accuracy. To further refine navigation, WAPPOS introduces DBSCAN-PP (Density-Based Spatial Clustering of Applications with Noise for Path Planning). This novel clustering algorithm identifies optimal waypoints by analyzing spatial patterns and building density, which are then transmitted to the UAV for adaptive navigation. A comparative study with onboard Deep Reinforcement Learning (DRL)-based path planning demonstrated that WAPPOS reduced CPU, GPU & Battery usage significantly and extended flight time by 4.4 minutes. By leveraging off-board computation, data-driven segmentation, and knowledge-driven clustering, WAPPOS reduces onboard computation, improves flight efficiency, and enhances UAV-based network deployment in disaster-stricken regions.

An efficient traffic light control system (TLCS) is an integral part of monitoring the flow of autonomous vehicles (AVs) through road junctions. Existing TLCS systems have limitations, focusing on specific scenarios like emergency vehicles or considering only the queue length at intersections. Furthermore, the absence of privacy protection in these systems exposes vehicles to potential tracking risks. We propose an intuitive and privacy-preserving TLCS (IPTLCS) to improve the performance of the TLCS for multiple traffic scenarios and prevent tracking of vehicles at traffic signals. The proposed IPTLCS uses a deep Q-learning (DQN) algorithm based on the quantity versus priority concept, adaptive to all scenarios, including pedestrians and emergency vehicles, making the framework applicable to real-time situations. Further, we propose a novel anonymity preserving protocol (APP) to protect the privacy of vehicles using two-party computation (2PC) that can prevent the tracking of AVs in our environment. Extensive experimental studies of the IPTLCS reveal that the model can achieve a reduced waiting time of 3.45 s/vehicle, a queue length of 3.32 vehicles/lane, a run time of 0.002 s, and a communication overhead of 3 KB. The increase in the efficiency of the proposed IPTLCS model in comparison with existing models in terms of waiting time, queue length, run time, and communication overhead is 11.08%–43.25%, 2.35%–24.23%, 33.3%–65.5%, and 38.77%–54.38%, respectively. Impact Statement The infusion of deep learning (DL) methodologies into traffic light systems signifies a substantial leap in advancing the management of AVs on roadways. Confronting the limitations of current traffic light systems, especially in adapting to diverse scenarios with varying quantities and priorities of vehicles, this study introduces a framework for efficient AV navigation that minimizes traffic collisions with reduced delays. Incorporating the proposed privacy measures ensures vehicular data protection, enhancing the overall system’s safety. Results demonstrate that the proposed method addresses the secure management of vehicular data and achieves reduced processing times, paving the way for progressing more intelligent and secure urban mobility solutions and fostering a seamless coexistence between AVs and pedestrians.

Unmanned Aerial Vehicles (UAVs) provide a good base platform for dynamic vision-based target detection. The detection process can be enhanced by utilizing multiple UAVs. In such scenarios, precise trajectory planning is essential to achieve objectives while avoiding collisions with obstacles. Furthermore, in most cases, Air-to-Air (A2A) detection of micro-UAVs in large-scale environments is challenging due to their dynamic movements and other complex parameters, such as poor light, motion blur, and occlusion. To solve these challenges, developing an algorithm that can adapt the control strategy based on changing system and environmental parameters is essential. This paper proposes an Adaptive Model Predictive Control (AMPC)-driven hybrid GANYOLOX framework to mitigate these navigation and detection challenges. The proposed AMPC allows model updates during multi-UAV operations, which enables estimation techniques to predict changes in the system model and helps to compute the target UAV position. Alternatively, the framework constructs a hybrid GAN-YOLOX model to overcome various A2A detection challenges. Extensive comparative analysis of the hybrid GANYOLOX framework achieved 94% detection accuracy and out-performed existing DL frameworks by 8%.

Autonomous Vehicles (AVs) have revolutionized transportation by utilizing 6G technologies such as automated driving assistance, navigation, connected intelligence, and independent decision-making. Yet, the increasing reliance on AVs exposes the Internet of Vehicles (IoV) to potential vulnerabilities, making it susceptible to cyber attacks. One prominent threat is Distributed Denial of Service (DDoS) attacks, which can significantly impact AVs' safety and operational integrity. DDoS attacks directly disrupt the fundamental functionality of AVs to make timely and informed decisions, potentially leading to accidents or system failures. Despite the existence of numerous systems for detecting DDoS attacks, their continuous evolution in various attack patterns poses a significant challenge for effective detection. This paper provides a vision of 6G Security by proposing an Advanced DDoS Attack Detection System (ADADS) to enhance the detection capabilities of DDoS attacks by employing a Hybrid Detection Model (HDM) and a Continuous Learning Model (CLM) to adapt the evolving patterns of DDoS attacks over time dynamically. The collaborative integration of these models leverages the overall efficiency of DDoS attack detection, delivering a robust and adaptive defense mechanism. The experimental findings reveal that the proposed ADADS achieves a remarkable accuracy 98.7% with rapid stabilization in a few iterations for the current 6G specifications and applications.

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.

Animal poaching poses a significant threat to wild animals, resulting in a rapid decrease in their populations. Unmanned Aerial Vehicles (UAVs) are extensively used to tackle illegal poaching. However, many potential security threats exist concerning transferring a huge amount of data between UAVs and forest officials. To address these challenges and enable secure transmission of big data from the UAVs, a Thermal Vision-based UAV and Blockchain (TVUB) aided poaching prevention system has been proposed in this paper. The TVUB system deploys a UAV swarm equipped with heat-sensing Thermal Infrared Radiation (TIR) sensors that run a Convolutional Neural Network (CNN)-based YOLOv4 image recognition model. The Deep Learning (DL) model is used to detect the presence of poachers based on their thermal images. Furthermore, the system deploys a novel blockchain-CNN mechanism, incorporating a smart contract that initializes the CNN framework through a serialized version of the YOLOv4 model. The execution of the poacher detection model proceeds in a decentralized manner due to the blockchain mechanism, thereby enhancing the security of big data transmitted. Extensive performance evaluation demonstrated the effective working of the TVUB system, which detected poachers with an accuracy of 96.4%.

Recently, Unmanned Aerial Vehicles (UAVs) have been deployed in various logistics and surveillance applications. Sixth-Generation (6G) cellular networks can further enhance communications to provide ubiquitous coverage, low-latency control, and seamless connectivity among the UAVs. However, achieving constant and end-to-end 3D coverage for user devices is demanding. UAV s have limited battery capacity; thus, energy consumption should be efficiently managed. Optimizing the UAV trajectories improves network performance by diminishing Base Station (BS) load or covering areas with limited radio access. Hence, we propose a Swarm Clustering and Double-Deep-Q-Network (SC-DDQN) framework for efficient communication in aerial networks. The framework constitutes a novel SC- Particle Swarm Optimization (SC-PSO) to improve intra-UAV communication and an Intelligent Trajectory Optimization (ITO) sub-component to optimize Air-to-Ground (A2G) trajectories. The results show that the proposed SC-DDQN framework achieves 40 % faster clustering and a 1.2 % failure probability of reaching a destination compared to the conventional systems, thus providing optimal clustering and trajectory for UAV communications.

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 Industrial Internet of Things (IIoT) is a new industrial 4.0 paradigm that combines IoT, robotics, cyber-physical systems, and other future industrial advancements. Unmanned aerial vehicles (UAVs), part of the IIoT infrastructure, have a significant potential for civil and military purposes. Through the artificial intelligence of things (AIoT), a well-organized group of UAVs outperforms a single large UAV in terms of device scalability, maintenance, and expense. Therefore, the UAV swarm with industry 4.0 intelligence can be used for a wide range of 24/7 security and remote monitoring applications. Though multi-UAV systems are beneficial, their application has many challenges. There is a high risk of collision in the multi-UAV system without coordination. This article proposes an AIoT-based navigation and formation control (AIoT-NFC) mechanism to scale down the collision risk by combining deep reinforcement learning (DRL) with the leader–follower approach. In AIoT-NFC, a deep deterministic policy gradient (DDPG) based algorithm is proposed to navigate UAVs in remote surveillance without colliding with obstacles and other UAVs. Furthermore, the AIoT-NFC system incorporates a fault tolerance mechanism that can handle the scenario of a leader's failure due to actuator malfunction. Experimental results show that the AIoT-NFC achieves faster convergence with a lower collision rate. AIoT-NFC reduced the collision rate by 14.99% compared to existing navigation methods in successful formation without colliding with the other UAVs.

Connected and Autonomous Vehicles (CAVs) enable various capabilities and functionalities like automated driving assistance, navigation and path planning, cruise control, independent decision making, and low-carbon transportation in the real-time environment. However, the increased CAVs usage renders the potential vulnerabilities in the Internet of Vehicles (IoV) environment, making it susceptible to cyberattacks. An Intrusion Detection System (IDS) is a technique to report network assaults by potential Autonomous Vehicles (AVs) without encryption and authorization procedures for internal and external vehicular communications. This paper proposes an Intelligent IDS (IIDS) to enhance intrusion detection and categorize malicious AVs using a modified Convolutional Neural Network (CNN) with hyperparameter optimization approaches for IoV systems. The proposed IIDS framework works in a 5G Vehicle-to-Everything (V2X) environment to effectively broadcast messages about malicious AVs. Thus IIDS aids in preventing collisions and chaos, enhancing safety monitoring in the traffic. The experimental results depict that the proposed IIDS achieves 98% accuracy in detecting attacks.

The immense growth of Autonomous Vehicles (AVs) and networking technologies have paved the way for advanced Intelligent Transportation Systems (ITS). AVs increase data demands from in-vehicle users, which pose a significant risk to the vehicular trajectory data and are extremely vulnerable to security threats. It is challenging to describe and detect the trajectory anomalies in urban motion behavior due to the enormous coverage and complexity of ITS in the V2X environment. Most existing systems rely on a restricted number of single detection strategies, such as determining frequent patterns and have limited accuracy in detecting anomalous trajectories. However, they focus only on outlier detection, failing to consider different patterns of anomalous trajectories. This paper proposes Efficient Trajectory Anomaly Detection and Classification (ETADC) framework in a 6G-V2X environment. The proposed ETADC framework employs the Deep Deterministic Policy Gradient algorithm (DDPG) to improve accuracy and efficiency by analyzing multiple strategies, namely driving speed, driving distance, driving direction, and driving time. The result analysis shows that the proposed ETADC technique outperforms the existing systems by 97% accuracy.

Unmanned Aerial Vehicles (UAVs), in conjunction with 6G, are a potential tool for monitoring agricultural lands and various agricultural applications. There have been several trajectory generation algorithms proposed for surveying agricultural land. However, in most situations, the accuracy of the trajectory is hampered by several factors, namely the complex geographical topography, performance, connectivity with the Ground Control Station (GCS) and positioning error of the UAV during flight, amongst others. Therefore, in this paper, we propose Drones Trajectory Generation employing an improved Genetic Algorithm (GA) and Non-Uniform Rational P-Splines (NURPS) based optimizer (DTG-GN). The improved GA utilizes a novel dual fitness function parameter to select an optimal path to map the weed-infested regions. The path chosen is often impeded by the high number of sudden turns, affecting the UAV’s speed profile and the path’s continuity. Therefore in the NURPS optimization, the computation of the intermediate knots vector between the control points improves the smoothness of the path. Furthermore, the accuracy of detecting the weed-infested area and the path length are employed to optimize the path. Besides, a 6G network is utilized for communicating the path between the GCS and the UAV to ensure seamless connectivity. Thus the time taken by DTG-GN to generate the optimal trajectory reduces by 38.815% for 50 control points. DTG-GN also reduces the average trajectory length by 45.67% for 50 control points, establishing its supremacy over conventional trajectory generation algorithms.

Internet of Things (IoT) is a system of interrelated sensors and computers to transfer data over a network. However, the sensors, Unmanned Aerial Vehicles (UAVs), and other IoT equipment used are susceptible to different security attacks. Dumb sensors are used to collect data in hostile environments. Dumb sensors are low powered sensors that lack computational power to perform cryptological operations. These sensors are preferred over high powered sensors due to their low electrical signature, but they have negligible computing power. To overcome the loss of authentication data due to node capture and lack of sensor location verification, we propose the Nexus of 6G and Blockchain for Authentication (NBA) system. The NBA system utilizes a permissioned blockchain-based network of UAVs and smart sensors to prevent code tampering. The system enables two-way trusted data transfer between UAVs and dumb sensors through a novel Hybrid Physical Unclonable Function Hashing (HPUFH) model. The system also utilizes a novel Pattern-based Signal Strength Correlation (PbSSC) algorithm to detect any unexpected location changes in the dumb sensor field. The extensive security and performance evaluation demonstrates that the proposed system is highly efficient and secure with a linear computational cost proportional to the number of challenge-response pairs.

Abstract Due to the rapid development of smart phone utilization, the use of fast response with delay sensitive applications leads to high traffic demands. These issues are not yet fulfill by current researchers. When there is a massive crowd of users gathering in hotspot areas such as, railway stations, corporate centers, and malls requires service accessing social networks, then there cause uncertain traffic with congestion and network delay. In this article, an Efficient Offloading Mechanism (EOM) is proposed to transfer the user's intensive computations to picocells, where each picocell (PE) is integrated with the functionalities of edge. Moreover, a novel distributed multihop mesh middle layer (DMMM) framework is proposed for seamless communication, where the self‐healing network provides high resilience, immediate response, and multiuser content sharing and saves battery power. Generally, a picocell is a distributed antenna system that is an alternative to the repeater utilized to extend wireless services to 100 users. An Adaptive Ant Optimization (AAO) algorithm is developed for backhaul routing conflicts. Simulation outcomes of EdgeCloudSim prove that the offloading tiny devices incorporated with considerable additional computational capacities to fulfill all the demands within 15 ms delay from each network.

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.

Autonomous Vehicles (AVs) are a significant part of Vehicular Adhoc NETwork (VANET) as they increase transportation accessibility. However, the presence of unpredictably sized potholes on road surfaces hampers the comfort and safety of autonomous navigation. Existing pothole avoidance mechanisms cannot dynamically adapt in unpredictable environments and do not comfort the traveler well in VANET. This paper proposes a novel Smart Pothole-Avoidance Strategy (SPAS) for safe navigation in a pothole-intensive environment. Potholes are avoided using the Deep Deterministic Policy Gradient (DDPG) algorithm as it performs best in continuous action space tasks and has a faster convergence speed. A Hybrid Recognition Model using the Speech and Gesture mechanism (HRM-SG) is proposed in this paper to collect the traveler’s real-time audio and visual feedback for the DDPG reward function. The received feedback aids in fine-tuning the model to avoid the pothole efficiently than the previous pothole. Traveler’s feedback is coupled with the vehicle’s sensor data and used to decide the time, speed, and angle at which lane change and speed change are executed. Finally, the SPAS continuously optimizes lane change parameters in VANET to achieve maximal traveler comfort during the operation. The result analysis indicates that SPAS achieves a 10-15% improvement in the accuracy of pothole avoidance, 10-12% higher comfort, and 8-10% faster convergence than the existing state-of-the-art techniques.

Connected and Autonomous Vehicles (CAVs) are an emerging solution to the issues of safe and sustainable transportation systems in the future. One major transport technology for CAVs is Cooperative Adaptive Cruise Control (CACC), for which unsignalized autonomous intersection crossing is a growing use case. CACC relies heavily on inter-vehicular communication and is thus vulnerable to message forgery and jamming attacks. Most solutions for CACC focus exclusively on enhancing efficiency or security but do not offer an integrated framework for achieving both on a large scale. In this paper, we propose a Blockchain-integrated Multi-Agent Deep Reinforcement Learning (Block-MADRL) architecture for enhancing the efficiency of CACC while cooperatively detecting attacks, reducing the fuel efficiency of identified attackers and securely notifying the overall network. Our approach uses multi-agent deep reinforcement learning to find fuel and throughput optimizing solutions for CACC and a cooperative verification mechanism based on Extended Isolation Forest (EIF) for attack detection. Attacker data is securely stored in a Road Side Unit (RSU) level blockchain, and we design a low-latency, high throughput consensus protocol for speedy and secure data dissemination. Simulation results indicate over 29.5% better lane throughput with our approach during acceleration forgery attack, up to 23% induced reduction in fuel efficiency of malicious vehicles, 17.6% higher blockchain throughput through our consensus protocol and over 8% improvement in attack detection rate compared to the state-of-the-art.

Unmanned Aerial Vehicles (UAVs), a new emerging form of Internet of Things (IoT), is a promising technology to be widely used in both civil and military applications. On the fly, the UAVs need to find an efficient and safe path by avoiding both static and dynamic obstacles to carry out any mission successfully. The Artificial Potential Field (APF) algorithm is one of the conventional catalysts in UAV path planning. However, APF-aided UAVs can be easily trapped into a local minimum solution before reaching the destination. Therefore, this paper proposes an efficient APF algorithm for Collision-free Path Planning (eAPF-CPP) in UAVs. In eAPF-CPP, the attractive and repulsive potentials evaluate the quadratic distance to the destination and the obstacle respectively. The evaluation aids the UAV to select the optimal path in navigation. The eAPF-CPP mechanism is simulated in the Software-In-The-Loop (SITL) setup, and the experimental results show that the eAPF-CPP mechanism utilizes an average of 24.4 seconds to track a safe path and has a lower collision rate of 8.56% compared with Artifical Potential Field Approach (APFA).

The massive increase in computing and network capabilities has resulted in a paradigm shift from vehicular networks to the Internet of Vehicles (IoV). Owing to the dynamic and heterogeneous nature of IoV, it requires efficient resource management using smart technologies, such as software-defined network (SDN), machine learning (ML), and so on. Roadside units (RSUs) in software-defined-IoV (SD-IoV) networks are responsible for network efficiency and offer several safety functions. However, it is not viable to deploy enough RSUs, and also the existing RSU placement lacks universal coverage within a region. Furthermore, any disruption in network performance or security impacts vehicular activities severely. Thus, this work aims to improve network efficiency through optimal RSU placement and enhance security with a malicious IoV detection algorithm in an SD-IoV network. Therefore, the memetic-based RSU (M-RSU) placement algorithm is proposed to reduce communication delay and increase the coverage area among IoV devices through an optimum RSU deployment. Besides the M-RSU algorithm, the work also proposes a distributed ML (DML)-based intrusion detection system (IDS) that prevents the SD-IoV network from disastrous security failures. The simulation results show that M-RSU placement reduces the transmission delay. The DML-based IDS detects the malicious IoV with an accuracy of 89.82% compared to traditional ML algorithms.

One of the most promising application areas of the industrial Internet of Things (IIoT) is vehicular ad hoc networks (VANETs). VANETs are largely used by intelligent transportation systems to provide smart and safe road transport. To reduce the network burden, software-defined networks (SDNs) act as a remote controller. Motivated by the need for greener IIoT solutions, this article proposes an energy-efficient end-to-end security solution for software-defined vehicular networks (SDVNs). Besides, SDN's flexible network management, network performance, and energy-efficient end-to-end security scheme plays a significant role in providing green IIoT services. Thus, the proposed SDVN provides lightweight end-to-end security. The end-to-end security objective is handled in two levels: 1) in roadside unit (RSU)-based group authentication scheme, each vehicle in the RSU range receives a group ID-key pair for secure communication; and 2) in private collaborative intrusion detection system (p-CIDS), the SDVN detects the potential intrusions inside the VANET architecture using collaborative learning that guarantees privacy through a fusion of differential privacy and homomorphic encryption schemes. The SDVN is simulated in NS2 and MATLAB, and results show increased energy efficiency with lower communication and storage overhead than existing frameworks. In addition, the p-CIDS detects the intruder with an accuracy of 96.81% in the SDVN.

Vehicular Ad hoc NETworks (VANETs) serve as the backbone of Intelligent Transportation Systems (ITS), providing passengers with safety and comfort. However, VANETs are vulnerable to major threats that affect data privacy and network services either from an individual or distributed attacker. In this paper, a Secure and Private-Collaborative Intrusion Detection System (SP-CIDS) is proposed to detect network attacks and to mitigate security concerns. In SP-CIDS, a Distributed Machine Learning (DML) model based on the Alternating Direction Method of Multipliers (ADMM) is used, which leverages the potential of vehicle-to-vehicle collaboration in the learning process to improve the storage efficiency, accuracy, and scalability of the IDS. However, there are significant data privacy concerns possible in such collaboration, where a CIDS can act as a malicious system that has access to the intermediate stages of the learning process. Additionally, the SP-CIDS system uses Differential Privacy (DP) technique to address the aforementioned data privacy risk associated with the DML-based CIDS. The SP-CIDS system is evaluated with logistic regression, naïve bayes, and ensemble classifiers. Simulation results substantiate that a private ensemble classifier secures the training data with DP and also achieves 96.94% accuracy.

Unmanned Aerial Vehicle (UAV) is an emerging technology that assists in various automated activities where human involvement is minimal. Though individual UAVs are extremely useful entities, their productivity can further be increased by deploying multi-UAVs. Pattern formation among multi-UAVs is one of the key functionalities in a swarm environment that is essential for several UAV missions namely military expedition, search and rescue operations, drone based delivery mechanisms etc. In this paper, to facilitate pattern formation among UAVs in an effective manner, a Time-Interleaved Pattern Formation (TIPF) Mechanism is proposed. The existing systems work for a fixed number of drones whose pattern switching mechanisms are preprogrammed. However, the TIPF mechanism enables switching patterns among dynamic number of drones (UAVs) on the fly by inducing a small delay between each UAV movement. The TIPF mechanism avoids collision, which occurs due to the simultaneous movement of UAVs. The proposed TIPF mechanism encompasses a Centralised Coordinate Calculation (CCC) algorithm to easily calculate the coordinates of UAVs in a given pattern. Further, this mechanism has also been simulated and tested in our proposed virtual IP based Software In The Loop (V-SITL) environment. This proposed V-SITL environment offers increased scalability on account of the entire UAV system being simulated in a single computer. The TIPF mechanism has been simulated for 8 drones in a dynamic manner for square and triangle patterns. The simulation results show that the pattern formation time avoids collision in a time interleaving rate of 52.63%.

A massive increase in the number of mobile devices and data-hungry vehicular network applications creates a great challenge for mobile network operators (MNOs) to handle huge data in cellular infrastructure. However, due to fluctuating wireless channels and high mobility of vehicular users, it is even more challenging for MNOs to deal with vehicular users within a licensed cellular spectrum. Data offloading in the vehicular environment plays a significant role in offloading the vehicle's data traffic from congested cellular network's licensed spectrum to the free unlicensed WiFi spectrum with the help of roadside units (RSUs). In this article, an intelligent reward-based data offloading in the next generation vehicular networks (IR-DON) architecture is proposed for dynamic optimization of data traffic and selection of intelligent RSU. Within the IR-DON architecture, an intelligent access network discovery and selection function (I-ANDSF) module with Q-learning, a reinforcement learning algorithm is designed. The I-ANDSF is modeled under a software-defined network (SDN) controller to solve the dynamic optimization problem by performing an efficient offloading. This increases the overall system throughput by choosing an optimal and intelligent RSU in the network selection process. The simulation results have shown the accurate network traffic classification, optimal network selection, guaranteed quality of service, reduced delay, and higher throughput achieved by the I-ANDSF module.

Millions of devices are becoming part of Internet of Things/5G. Securing these devices against all potential threats is a huge challenge. The 5G specification goals require rigid and robust security protocol against such threats. Quantum cryptography is a recently emerged term in which we test the robustness of security protocols against quantum computers. Therefore, in this article, we propose a security protocol called quantum key GRID for authentication and key agreement (QKG-AKA) scheme for the dynamic security association. This scheme is efficiently deployed in long term evolution architecture without any significant modifications in the underlying base system. The proposed QKG-AKA mechanism is analyzed for robustness and proven safe against quantum computers. The simulation results and performance analysis show drastic improvement regarding security and key management over existing schemes.

Growing popularity of multimedia applications, pervasive connectivity, higher bandwidth, and euphoric technology penetration among bulk of the human race that happens to be cellular technology users, has fueled the adaptation to long-term evolution (LTE)/system architecture evolution. The LTE fulfills the resource demands of the next generation applications for now. We identify security issues in authentication mechanism used in LTE that without countermeasures might give super user rights to unauthorized users. The LTE uses static LTE key to derive the entire key hierarchy, i.e., LTE follows Evolved Packet System-Authentication and Key Agreement based authentication, which discloses user identity, location, and other personally identifiable information. To counter this, we propose a public key cryptosystem named “International mobile subscriber identity Protected Console Grid based Authentication and Key Agreement (IPG-AKA) protocol” to address the vulnerabilities related to weak key management. From the data obtained from threat modeling and simulation results, we claim that the IPG-AKA scheme not only improves security of authentication procedures, but also shows improvements in authentication loads and reduction in key generation time. The empirical results and qualitative analysis presented in this paper prove that IPG-AKA improves security in authentication procedure and performance in the LTE.