Search Results (175)

Connected and autonomous vehicles (CAVs) increasingly rely on vehicle-to-everything (V2X) communication and distributed sensing infrastructures to support cooperative driving and intelligent transportation services. While these capabilities improve traffic efficiency and safety, they also expand the attack surface of vehicular networks and expose in-vehicle communication systems such as the Controller Area Network (CAN) bus to a wide range of cyber threats. Machine learning-based anomaly detection has emerged as a promising approach for identifying malicious CAN traffic patterns; however, conventional centralized learning requires large-scale data aggregation from vehicles, which raises privacy and scalability concerns. Federated learning (FL) enables collaborative model training across distributed vehicles without requiring the exchange of raw in-vehicle data, making it attractive for privacy-preserving vehicular security applications. Nevertheless, FL systems remain vulnerable to adversarial participants that manipulate local training data or model updates to poison the global model during aggregation. In this work, we present a systematic robustness evaluation of federated anomaly detection in connected vehicular networks under adversarial conditions. The study compares six aggregation strategies, including Federated Averaging (FedAvg), coordinate-wise Median, Trimmed Mean, Krum, Multi-Krum, and Geometric Median (GeoMed), within a non-IID federated CAN bus anomaly detection setting. The evaluation covers label-flipping attacks, gradient-scaling attacks, and a feature-triggered backdoor attack. In addition, the analysis examines malicious client participation, attack-strength variation, learning-rate sensitivity, Trimmed Mean beta sensitivity, multi-seed reliability, and server-side aggregation time. The results show that FedAvg is vulnerable under strong adversarial manipulation, while Trimmed Mean is sensitive to the selected trimming fraction. Median and GeoMed provide strong robustness against gradient-scaling attacks, whereas Multi-Krum achieves the strongest resistance to label-flipping and backdoor attacks. These findings demonstrate that no single aggregation strategy is optimal across all threat models. Instead, robust aggregation for federated CAV anomaly detection should be selected according to the expected attack type, reliability requirement, and computational overhead. Full article

The incorporation of artificial intelligence, multi-sensor perception, and cyber-physical control into mining operations offers tremendous opportunities for increasing productivity, safety, and sustainability. However, present frameworks focus on discrete subsystems rather than providing a unified, safety-constrained optimization method that has been verified in both surface and underground environments. This paper describes a scalable, hierarchical autonomous mining architecture that incorporates sensor fusion, edge intelligence, fleet coordination, and digital twin-based decision support. It is designed to operate in GNSS-denied conditions and extreme climatic constraints common to Nordic mining environments. A mathematical modeling approach formalizes vehicle dynamics, drilling mechanics, and multi-agent fleet coordination inside a safety-constrained multi-objective optimization formulation. The framework is validated using Monte Carlo simulation with uncertainty measurement, sensitivity analysis, and statistical hypothesis testing. The preliminary results show improvements over a typical baseline, with productivity increasing by approximately 24.3% ± 3.2%, energy consumption decreasing by 12.8% ± 2.5%, and safety risk decreasing by 48.6% ± 4.1%. A sensitivity study identifies localization accuracy, communication delay, and optimization weighting as the primary system performance drivers. The suggested framework serves as a reproducible and transferable reference model for next-generation intelligent mining systems, having direct applications to both industrial deployment and future research in autonomous resource extraction. Full article

The large-scale deployment of autonomous vehicles (AVs) in mixed-traffic environments raises an important question: how do human drivers evaluate safety when interacting with AVs under real-world uncertainty? This study aims to examine how drivers’ objective knowledge of AVs shapes their perceived safety when sharing the road with AVs in mixed-traffic environments. Using survey data from 905 licensed drivers in Wuhan, China, this study treats perceived road-sharing safety as an interaction-level evaluative outcome rather than merely a precursor of adoption intention. Latent class analysis was first used to identify knowledge-based driver segments, structural equation modeling was then applied to estimate Theory of Planned Behavior (TPB)-related psychological constructs, and ordered logit regression was finally employed to examine the determinants of perceived safety across segments. The results indicate that behavioral intention consistently shows a positive association with perceived safety; however, attitude toward AVs exhibits a significant negative association among high-knowledge drivers. This attitudinal reversal challenges the implicit homogeneity assumption embedded in conventional TPB applications and suggests that cognitive familiarity may recalibrate, rather than amplify, technological optimism. Overall, the findings show that knowledge-based heterogeneity changes the psychological mechanisms underlying safety appraisal in mixed traffic. These insights carry important implications for differentiated communication strategies and trust calibration in transitional automated mobility systems. Full article

Vehicular Ad Hoc Networks (VANETs) are a key enabler of intelligent transportation systems, supporting safety-critical and latency-sensitive applications through vehicle-to-vehicle and vehicle-to-infrastructure communications. However, high node mobility, rapidly changing network topologies, and heterogeneous wireless conditions pose significant challenges to traditional distributed networking approaches, particularly in terms of quality of service (QoS) stability and handover performance. Software-Defined Networking (SDN) offers promising solutions by enabling centralized control, programmability, and flexible deployment of network functions. This paper presents an SDN-enabled vehicular networking platform designed for realistic, system-level experimentation under dynamic mobility conditions. The proposed platform tightly couples microscopic vehicular mobility generated by SUMO with wireless network emulation in Mininet-WiFi, enabling real-time interaction between vehicle movement, wireless connectivity, and SDN control decisions, where a custom SDN controller implements mobility-aware traffic management and handover handling across roadside units. Extensive experimental scenarios evaluate throughput, packet loss, jitter, and end-to-end latency under varying traffic loads and mobility patterns. Results indicate that SDN-based centralized control improves QoS consistency relative to the unmanaged baseline configuration considered in this study. The proposed platform provides practical insights and a reproducible experimental framework for the design and evaluation of software-defined vehicular networking systems. Full article

The Cooperative-Intelligent Transport Systems (C-ITS) require emerging Vehicular-to-Everything (V2X) applications, such as Advanced Driving Systems (ADS) and Connected Autonomous Driving (CAD), to support efficient road safety measures. These applications often require high reliability, throughput, and low latency by exchanging a significant amount of data among End-to-End (E2E) vehicles. However, current V2X communication technologies, such as DSRC and C-V2X, are not able to meet these stringent demands. Two or more Radio Access Technologies (RATs) are essential to guarantee the required Quality of Service (QoS) in high-density vehicular environments. To address this critical gap, this survey presents the 3M Framework—a hybrid vehicular architecture approach based on Multi-Radio Access Technology (M-RAT), Mobility Management, and Machine Learning (ML). The manuscript provides a detailed overview of V2X Multi-RAT evolutions, analyzing their state-of-the-art and limitations in heterogeneous scenarios. We specifically highlight that the existing Long Term Evolution (LTE)-based mobility management fails to meet V2X handover requirements for high-speed vehicles, necessitating a comprehensive overview of Vertical Handover (VHO). Furthermore, the survey details how the integration of ML promotes the prediction of network states, enabling optimized context-aware decisions for connectivity and resource allocation, thereby reducing Handover Failures (HoFs) and enhancing reliability using techniques like Deep Reinforcement Learning (DRL). Finally, based on a comprehensive review of existing methods, the paper identifies critical research directions and challenges required to realize intelligent, hyper-fast, and ultra-reliable Beyond 5G (B5G) and Sixth Generation (6G) V2X networks, delivering a more profound understanding for future endeavors. Full article

Vehicle networking is a new paradigm in wireless technology that facilitates communication between vehicles in close proximity and in-vehicle internet access. This technology paves the way for a variety of safety, convenience and entertainment applications, including safety message exchange, real-time traffic information sharing and public internet access. The overall goal of vehicular networks is to create an efficient, safe and convenient environment for vehicles on the road. This paper presents a Sensitive Node Election (SNE) algorithm adapted to routing protocols in certain opportunistic network environments. The algorithm focuses on selecting the best agent for communication using an innovative approach for message forwarding. Quality of Service (QoS) metrics targeted for optimization include network end-to-end throughput and packet delivery, with the aim of improving the overall performance of the network. Our algorithm includes a stochastic rebroadcasting scheme that takes into account parameters, such as vehicle density, distance between vehicles and transmission distance, and adapts to various network conditions. Furthermore, the SNE algorithm uses a metric based on transmission distance and can dynamically adapt to application requirements, such as prioritization. It provides high throughput and minimizes delay. The results demonstrate the effectiveness of this approach in improving QoS in various vehicular ad hoc network (VANET) simulations and influencing the neural network ensemble (NNE Algorithm). Full article

Nowadays, thermal runaway accidents involving lithium batteries in new energy vehicles and energy storage power stations occur frequently, with battery deformation pressure as the core precursor signal. Traditional battery protection schemes suffer from limitations, including wired connections, limited real-time remote monitoring, and insufficient sensing accuracy, rendering them unable to meet the safety monitoring needs of large-scale battery modules. Therefore, a high-precision pressure-sensing battery protection system based on the Internet of Things has been developed. This paper selects a MEMS high-precision pressure sensor with an accuracy of ±0.1 kPa to design an IoT sensing node based on the STM32L431 and LoRa/Wi-Fi 6, integrating pressure sensing and wireless communication. It proposes a sliding-average filtering and wavelet denoising algorithm, as well as a temperature-compensation calibration model, to optimize sensing accuracy. Additionally, it constructs a hierarchical early warning model based on pressure thresholds. The experiment demonstrates that the sensor achieves a detection accuracy of 99.2%, a response delay of less than 50 ms, a transmission packet loss rate of less than 0.5%, an end-to-end delay of less than 200 ms, and an early warning accuracy rate of 99.2% under battery overcharge/overtemperature conditions. The innovation of this study lies in the first integration of high-precision pressure sensing and IoT communication for battery protection. A low-power IoT sensing node tailored for battery aging scenarios has been designed, validating the novel application value of IoT sensing in the safety monitoring of new energy equipment. This system fills a gap in IoT pressure-sensing technology for battery protection, enabling practical applications and serving as a reference for implementing integrated sensing and communication technology. Full article

With the advancement of UAV (Unmanned Aerial Vehicle) technology, flying ad-hoc networks (FANETs), composed of multiple coordinating UAVs, demonstrate tremendous application potential in disaster relief, environmental monitoring and intelligent logistics. However, inherent resource constraints and unpredictable operating environments make UAV failures a frequent and critical challenge. Particularly in mission-critical applications, simultaneous or consecutive failures of multiple UAVs can severely disrupt network topology, leading to catastrophic consequences such as network fragmentation and service interruptions. Furthermore, traditional topology reconstruction algorithms suffer from high computational overhead and significant communication delays. Primarily designed for single-node failure recovery, they are ill-equipped to address the challenge of concurrent multi-node failures. To address these challenges, this paper proposes a topology reconstruction algorithm tailored for multi-node failure scenarios in FANETs. The core objective of this algorithm is to minimize communication overhead and secondary damage to the network during the reconstruction process while ensuring basic reconstruction results, thereby improving the system’s energy efficiency and robustness. The proposed framework integrates three key phases: First, overlapping communication coverage areas among neighbors of failed nodes are leveraged to define first and second regions, enabling rapid identification of connection restoration candidate positions and avoiding computationally intensive global calculations. Second, a comprehensive importance evaluation mechanism is constructed based on the topological and functional attributes of node, categorizing nodes into different importance types. For failed nodes of varying importance, differentiated search ranges and retry strategies are employed to ensure the most suitable nodes are selected for reconstruction tasks. Third, the inflexibility of repulsion ranges in traditional artificial potential field (APF) method is addressed by introducing dynamic repulsion influence zones and a composite repulsion model. The improved APF algorithm enhances safety in high-speed scenarios and reduces the probability of UAVs becoming trapped in local minima. Finally, extensive simulations validate that the proposed algorithm accurately identifies critical network nodes and promptly implements effective reconstruction measures to minimize network damage. Full article

The escalating global rates of traffic accidents in urban areas and the growing demands of smart cities underscore the urgent need for advanced real-time monitoring solutions. This paper presents an EdgeML-based system for vehicle tracking that performs real-time speed and distance analysis and traffic violation detection. This is achieved by deploying a YOLOv8 object detection model on a Raspberry Pi 5 with a Coral USB Edge TPU accelerator. The system integrates computer vision and IoT technologies to enable real-time processing. It utilizes the Message Queuing Telemetry Transport (MQTT) protocol to allow scalable communication between distributed edge devices and a central MongoDB database, facilitating real-time storage and analysis of traffic data. A synthetic dataset generated via the Blender 3D modeling tool validates the system’s accuracy, demonstrating average speed and distance measurement errors of ±2.11 km/h and ±0.58 m, respectively. These findings are further supported by preliminary practical experiments in a real-world environment, where speed estimation errors remained within 0–2 km/h and distance errors stayed below 0.11 m. Key innovations of this work include license plate recognition, speeding and collision detection, and context analysis using Google’s Gemini-2.5-Flash API. A Streamlit dashboard provides real-time visualization of traffic metrics, violations, and aggregated data. A comparative evaluation of YOLOv5n, YOLOv8n, YOLOv11n, and YOLOv12n identifies YOLOv8n as the most suitable model for embedded deployment, achieving 91.07 ± 0.61% mAP@0.5 without quantization, 88.77 ± 3.31% mAP@0.5 with quantization, while maintaining real-time performance of 30–43 frames per second (FPS) on the Edge TPU. The system’s modular architecture, low latency, and robust performance highlight its suitability for smart city applications, enhancing traffic safety and enabling data-driven urban mobility management. Full article

Unmanned aerial vehicle (UAV) applications are growing fast in different sectors, such as agricultural, commercial, academic, leisure, and health fields. However, drones pose a significant threat to public safety due to their ability to transmit information, particularly when used in an unauthorized or malicious manner. In fact, in order to protect citizens’ privacy and prevent accidents in high-traffic areas due to poorly controlled flights, no-fly zones for drones have been established in the legislation of a number of countries. Most common UAV detection techniques are based on radio frequencies, which identify drones and their models by monitoring radio frequency signals. However, differentiating between multiple UAVs of the same model is their main limitation. This article fills this gap by proposing a method for physically tracking the communication frames of a registered UAV in the presence of another UAV of the same model. A measurement campaign was conducted to collect real-world RF communication signals from two DJI MAVIC 2 Zoom, two DJI Air2S, and two DJI Phantom drones. This measurement was performed inside and outside an anechoic chamber in order to study the UAV’s communication without any interference and in the presence of other communications. Through detailed statistical analysis, we characterized features such as communication duration, time intervals between communications, signal strength, and patterns in communication timing sequences. Our analysis revealed unique, identifiable patterns for each UAV, even within identical models. Based on these results, we developed an automated system that links communication frames to the corresponding registered drones. The proposed method fills gaps in drone detection and surveillance models, providing valuable information for applications in the fields of security and airspace management. This research lays the foundation for drone identification solutions, thereby addressing a major limitation of current detection technologies. Full article

The rapid advancement of connected and autonomous vehicles (CAVs) has the potential to revolutionize road transportation, promising significant improvements in safety, efficiency, and sustainability. However, traditional CAV architectures are predominantly modular and rule-based. They struggle with interaction, cooperation, and adaptability in complex mixed-traffic environments. Moreover, the substantial infrastructure investment required and the absence of compelling killer applications have limited large-scale deployment of CAVs and roadside units (RSUs), resulting in insufficient penetration to realize the full safety benefits of CAV applications and creating a deployment stalemate. To address the above challenges, this paper proposes an innovative connected autonomous vehicle system, termed AQ-CAV, which leverages recent advances in AI agents and QR codes. AI agents are employed to enable cooperative, self-adaptive, and intelligent vehicular behavior, while QR codes provide a cost-effective, accessible, robust, and scalable mechanism for supporting CAV deployment. We first analyze existing CAV systems and identify their fundamental limitations. We then present the architectural design of the AQ-CAV system, detailing the components and functionalities of vehicle-side and infrastructure-side agents, inter-agent communication and coordination mechanisms, and QR code-based authentication for AQ-CAV operations. Representative applications of the AQ-CAV system are investigated, including a case study on emergency response. Preliminary results demonstrate the feasibility and effectiveness of the proposed system, which achieves significant safety improvements at low system cost. Finally, we discuss the key challenges faced by AQ-CAV and outline future research directions that require exploration to fully realize its potential. Full article

Unmanned Aerial Systems (UASs) are playing an increasingly critical role in both civilian and defense applications. However, their heavy reliance on unencrypted Global Navigation Satellite System (GNSS) signals, particularly GPS, makes them highly susceptible to signal spoofing attacks, posing severe operational and safety threats. This paper introduces a comprehensive, AI-driven multi-layer sensor framework that simultaneously enables real-time spoofing detection and secure command-and-control (C2) communication in lightweight UAS platforms. The proposed system enhances telemetry reliability through a refined preprocessing pipeline that includes a novel GPS Drift Index (GDI), robust statistical normalization, cluster-constrained oversampling, Kalman-based noise reduction, and quaternion filtering. These sensing layers improve anomaly separability under adversarial signal manipulation. On this enhanced feature space, a differentiable architecture search (DARTS) approach dynamically generates lightweight neural network architectures optimized for fast, onboard spoofing detection. For secure command and control, the framework integrates a low-latency cryptographic layer utilizing PRESENT-128 encryption and CMAC authentication, achieving confidentiality and integrity with only 1.79 ms latency and a 0.51 mJ energy cost. Extensive experimental evaluations demonstrate the framework’s outstanding detection accuracy (99.99%), near-perfect F1-score (0.999), and AUC (0.9999), validating its suitability for deployment in real-world, resource-constrained UAS environments. This research advances the field of AI-enabled sensor systems by offering a robust, scalable, and secure navigation framework for countering GPS spoofing in autonomous aerial vehicles. Full article

The rapid proliferation of connected vehicles equipped with both Vehicle-to-Vehicle (V2V) sidelink and cellular interfaces creates new opportunities for real-time vehicular applications, yet achieving ultra-reliable communication without prohibitive cellular costs remains challenging. This paper addresses reliable inter-vehicle video streaming for safety-critical applications such as See-Through for Passing and Obstructed View Assist, which require stringent Service Level Objectives (SLOs) of 50 ms latency with 99% reliability. Through measurements in Seoul urban environments, we characterize the complementary nature of V2V and Vehicle-to-Network-to-Vehicle (V2N2V) paths: V2V provides ultra-low latency (mean 2.99 ms) but imperfect reliability (95.77%), while V2N2V achieves perfect reliability but exhibits high latency variability ($P_{99}$: 120.33 ms in centralized routing) that violates target SLOs. We propose a hybrid framework that exploits V2V as the primary path while selectively retransmitting only lost packets via V2N2V. The key innovation is a dual loss detection mechanism combining gap-based and timeout-based triggers leveraging Real-Time Protocol (RTP) headers for both immediate response and comprehensive coverage. Trace-driven simulation demonstrates that the proposed framework achieves a 99.96% packet reception rate and 99.71% frame playback ratio, approaching lossless transmission while maintaining cellular utilization at only 5.54%, which is merely 0.84 percentage points above the V2V loss rate. This represents a $7\times$ cost reduction versus PLR Switching (4.2 GB vs. 28 GB monthly) while reducing video stalls by $10\times$. These results demonstrate that packet-level selective redundancy enables cost-effective ultra-reliable V2X communication at scale. Full article

Unmanned ground vehicles (UGVs) in agriculture face challenges in navigating complex environments due to the presence of dynamic obstacles. This causes several practical problems including mission delays, higher energy consumption, and potential safety risks. This study addresses the challenge by shifting path planning from reactive local avoidance to proactive global optimization. To that end, it integrates aerial imagery from an unmanned aerial vehicle (UAV) to identify dynamic obstacles using a low-latency YOLOv8 detection pipeline. These are translated into georeferenced exclusion zones for the UGV. The UGV follows the optimized path while relying on a LiDAR-based reactive protocol to autonomously detect and respond to any missed obstacles. A farm management information system is used as the central coordinator. The system was tested in 30 real-field trials in a walnut orchard for two distinct scenarios with varying worker and vehicle loads. The system achieved high mission success, with the UGV completing all tasks safely, with four partial successes caused by worker detection failures under afternoon shadows. UAV energy consumption remained stable, while UGV energy and mission time increased during reactive maneuvers. Communication latency was low and consistent. This enabled timely execution of both proactive and reactive navigation protocols. In conclusion, the present UAV–UGV system ensured efficient and safe navigation, demonstrating practical applicability in real orchard conditions. Full article

Interest in the adoption of autonomous vehicles (AVs) continues to grow. It is essential to design new software architectures that meet stringent real-time, safety, and scalability requirements while integrating heterogeneous hardware and software solutions from different vendors and developers. This paper presents a lightweight, modular, and scalable architecture grounded in Service-Oriented Architecture (SOA) principles and implemented in ROS 2 (Robot Operating System 2). The proposed design leverages ROS 2’s Data Distribution System-based Quality-of-Service model to provide reliable communication, structured lifecycle management, and fault containment across distributed compute nodes. The architecture is organized into Perception, Planning, and Control layers with decoupled sensor access paths to satisfy heterogeneous frequency and hardware constraints. The decision-making core follows an event-driven policy that prioritizes fresh updates without enforcing global synchronization, applying zero-order hold where inputs are not refreshed. The architecture was validated on a 1:10-scale autonomous vehicle operating on a city-like track. The test environment covered canonical urban scenarios (lane-keeping, obstacle avoidance, traffic-sign recognition, intersections, overtaking, parking, and pedestrian interaction), with absolute positioning provided by an indoor GPS (Global Positioning System) localization setup. This work shows that the end-to-end Perception–Planning pipeline consistently met worst-case deadlines, yielding deterministic behaviour even under stress. The proposed architecture can be deemed compliant with real-time application standards for our use case on the 1:10 test vehicle, providing a robust foundation for deployment and further refinement. Full article