Search Results (224)

Vehicle-to-everything (V2X) communication systems impose stringent latency and reliability requirements that are difficult to satisfy in highly dynamic wireless environments. Although reconfigurable intelligent surfaces (RISs) and unmanned aerial vehicles (UAVs) have independently demonstrated potential in enhancing wireless coverage, most existing RIS–UAV frameworks rely on idealized assumptions such as perfect channel state information (CSI) and static user scenarios. In this paper, a multi-RIS-assisted UAV-enabled V2X communication framework is proposed that explicitly accounts for vehicular mobility, latency constraints, and mobility-induced CSI aging. Multiple RIS panels are cooperatively deployed to eliminate coverage blind spots and ensure link continuity in realistic V2X environments. A joint UAV mobility and RIS phase optimization approach is proposed under outdated CSI to improve link reliability. Additionally, a time-varying performance analysis is carried out for understanding the dynamic behavior of signal-to-noise ratio (SNR) and average bit error rate (ABER) for mobility-aware CSI aging. Simulation results demonstrate that the proposed framework reduces the ABER by approximately 75% compared to a conventional single-RIS system under outdated CSI at 20 dB SNR ($1.07\times {10}^{-1}$ vs. $4.32\times {10}^{-1}$), while substantially suppressing outage intervals in high-mobility V2X scenarios ($v=20$ m/s, CSI delay $\tau =20$ ms), confirming the effectiveness of cooperative multi-RIS assistance for safety-critical vehicular communications. Full article

Urban traffic congestion presents a complex challenge driven by intricate spatial dependencies and non-stationary temporal dynamics. While Multi-Agent Deep Reinforcement Learning has shown promise for Traffic Signal Control, existing approaches often struggle with partial observability and fail to coordinate effectively across large-scale, heterogeneous road networks. In this paper, we propose HydraLight (HYbrid Deep Reinforcement Learning Architecture for Traffic Lights), a novel spatio-temporal framework that integrates Graph Attention Networks and Temporal Transformers. To overcome the localized myopia of standard graph methods, HydraLight introduces a Global Pooling Context module that broadcasts macroscopic, citywide traffic summaries, enabling agents to proactively mitigate systemic gridlock. Furthermore, to facilitate robust multi-scenario training, we introduce a Unified Prioritized Experience Replay (Unified PER) module that normalizes Temporal-Difference errors, preventing task dominance across diverse topologies. Extensive experiments on the RESCO benchmark across five synthetic and real-world networks demonstrate that HydraLight consistently outperforms state-of-the-art baselines (including X-Light and CoSLight).Byreducing traffic congestion, travel delays, and idle waiting times, the proposed framework also contributes to more sustainable urban mobility through improved traffic flow efficiency, lower fuel consumption, and reduced vehicular carbon emissions. Notably, the proposed architecture excels in structurally irregular environments, achieving up to 13.07% reduction in average travel time on complex arterial networks and consistently improving queue stability and waiting-time minimization across both synthetic and real-world RESCO benchmarks compared to state-of-the-art baselines. Full article

A three-year (January 2020–December 2022) daily dataset of 16 polycyclic aromatic hydrocarbons (PAHs) collected in parallel with PM_2.5 and a suite of meteorological variables at a coastal Mediterranean urban site in southern Italy (Pomigliano d’Arco, Campania) is presented and analysed. Raw PAH time series were decomposed into a long-term trend component (LT), a seasonal component (ST), and a residual component (RT) using an iterative missing-value-robust Kolmogorov–Zurbenko (KZ) moving-average filter. Spearman rank correlations between PAH concentrations and four meteorological predictors (mean temperature, relative humidity, mean wind speed, and maximum wind speed) were computed for each congener. Diagnostic molecular ratios—Fla/(Fla + Pyr), BaP/BghiP, Indeno[1,2,3-cd]pyrene/(IcdP + BghiP), and BaA/(BaA + Chr)—were evaluated seasonally and interpreted jointly with an information-theoretic Bayesian mixture modelling procedure (SNOB/MML) and with the documented susceptibility of some PAH ratios, especially BaP-containing ratios, to atmospheric ageing, phase repartitioning and summer photodegradation. Total PAH concentrations (sum of 16 congeners) ranged from <1 ng m⁻³ in summer to 46 ng m⁻³ during winter high-pollution episodes, with BaP peaking at ≈6.7 ng m⁻³. Because BaP was measured in the PM_2.5 fraction, comparisons with the EU annual target value of 1 ng m⁻³ established for PM₁₀-bound BaP are treated as indicative context only, not as formal compliance statements. Pronounced seasonal variability was driven primarily by residential heating emissions, and the incremental lifetime cancer risk (ILCR) for inhalation exposure reached $1.03\times {10}^{-4}$ (95% CI: $0.88$–$1.20\times {10}^{-4}$) during the heating season under a continuous outdoor-exposure worst-case scenario. The absolute ILCR magnitude is conditional on the selected TEF scheme and on the adopted BaP unit-risk coefficient; under an additional indoor-dominated scenario (16 h day⁻¹, infiltration factor 0.6), the corresponding risk remained above the conventional ${10}^{-6}$ benchmark. An anomalous near-background PAH signal during spring 2020 is attributed to the COVID-19 national lockdown, which reduced total PAH concentrations by approximately 85% relative to the seasonal component predicted by the iterative moving-average filter for the same calendar window. Source apportionment via diagnostic ratios identifies residential/biomass combustion as the dominant cold-season source and vehicular emissions as the prevailing warm-season source. These results provide a novel characterisation of PAH pollution dynamics in the undersampled southern Mediterranean and provide evidence to support targeted abatement policies. Full article

In urban environments with signal blockage and multipath effects, GNSS observation errors often exhibit temporal correlation. The Gaussian white noise assumption adopted in conventional tightly coupled Kalman filtering is prone to model mismatch under such conditions, which may lead to an underestimation of state uncertainty and consequently cause the protection level (PL) to fail to reliably bound the true positioning error. To address this issue, this paper proposes a tightly coupled GNSS/INS integrity monitoring method based on state augmentation and frequency-domain constrained parameter tuning. The method introduces first-order Gauss-Markov processes (GMP) to model major time-correlated error sources, including residual ephemeris and clock errors, residual tropospheric delay, and code multipath, by augmenting them into the filter state for joint estimation. The model parameters are further conservatively tuned based on power spectral density (PSD) envelope constraints to obtain more consistent covariance estimates. Based on this, the covariance output from the augmented filter is incorporated into the multiple hypothesis solution separation (MHSS) framework, enabling the protection level computation to better match the actual error statistics. Experimental results using vehicular field test data show that the proposed method effectively improves estimation consistency and significantly reduces the risk of PL underestimation in degraded environments. Furthermore, it achieves reliable bounding of horizontal positioning errors without noticeable degradation in positioning accuracy, while maintaining good system availability. These results demonstrate the effectiveness of covariance construction based on physical error modeling and PSD envelope constraints for integrity monitoring in complex environments. Full article

Ground-based scatterometers are widely used for quantitative microwave backscattering measurements in soil moisture retrieval, vegetation monitoring, and satellite scatterometer validation. However, low-cost software-defined radio (SDR) transceivers provide limited instantaneous bandwidth, making it difficult to transmit and process signals with bandwidths on the order of hundreds of MHz for fine range resolution, especially for systems requiring real-time onboard processing. To address this problem, this paper presents a vehicular, fully polarimetric, SDR-based scatterometer that achieves an equivalent wideband response by sequentially transmitting adjacent narrow subbands and coherently synthesizing them onboard. To enable real-time operation on a resource-limited field-programmable gate array/system-on-chip (FPGA/SoC) platform, we adopt a frequency-domain synthesis-pulse-compression pipeline that avoids interpolation and eliminates repeated matched filtering across subbands. A slot-based online phase calibration is performed within the settling window after each fast lock to estimate and compensate random local oscillator (LO) phase offsets, preserving coherent stitching. In addition, pulse repetition within each subband and coherent accumulation are integrated to improve the signal-to-noise ratio (SNR) under real-time throughput constraints. A Zynq-based implementation demonstrates deterministic onboard range-profile output, with a minimum processing latency of about 1.57 ms per frame. Loopback and outdoor experiments validate the equivalent 200 MHz bandwidth (five 40 MHz subbands), achieving approximately 0.75 m resolution and yielding sidelobe metrics consistent with the designed windowing, including a peak sidelobe ratio (PSLR) of −27.43 dB and an integrated sidelobe ratio (ISLR) of −12.38 dB. Field scans over farmland further show consistent ${\mathsf{\sigma }}_0$ trends across incidence angle and azimuth, indicating reliable onboard quantitative backscattering measurement. These results demonstrate that the proposed method provides a feasible solution for deterministic real-time equivalent wideband scatterometry on a low-cost SDR platform. Full article

At racing speeds above 300 km/h (≈83 m/s), hazard awareness becomes a vehicular-communications problem: 100 ms already correspond to about 8.3 m of blind travel before an alert can influence braking, line choice, or torque delivery. Cloud-only telemetry is therefore insufficient under intermittent coverage and variable round-trip delay, while conventional trackside and pit-wall links do not provide direct inter-bike hazard dissemination. We propose Hybrid Epistemic Offloading (HEO), an edge–mesh–cloud architecture for high-mobility V2V/V2X hazard dissemination that explicitly separates an ephemeral safety plane from a durable cloud-analytics plane. On-bike edge nodes ingest high-rate ECU/IMU signals over CAN and persist full-fidelity traces into standardized ASAM MDF containers, enabling loss-tolerant buffering, deterministic replay, and post hoc auditability across coverage gaps. For real-time safety, motorcycles form a local V2V mesh that disseminates compact hazard digests using latency-bounded gossip with adaptive fanout, TTL-based suppression, and redundancy-aware forwarding over sidelink-capable V2X links. The hazard channel is formulated as uncertainty-aware to account for localization error and propagation delay at race pace. We evaluate the system in two stages: (i) a reproducible mobility-coupled simulation/emulation campaign for mesh dissemination and durable edge → gateway → cloud delivery; and (ii) an MDF4 replay-based Jerez pilot for stability-oriented co-design analysis. Under the tested conditions, the durable MQTT path achieved an 83.4 ms median, 175.9 ms p95, and 303.74 ms maximum end-to-end latency with no observed event loss. In the Jerez pilot, the co-design workflow reduced mean wheel slip from 6.26% to 3.75% (−40.10%) and a control-volatility proxy from 0.1290 to 0.0212 (−83.58%). Full article

Uninterrupted traffic flow monitoring is a prerequisite for optimal resource allocation and minimizing vehicular emissions in smart cities. However, centralized traffic management architectures are highly vulnerable to single points of failure. When structural sensor malfunctions occur, the resulting network unobservability paralyzes dynamic signalization, triggering cascading traffic congestion, extended idling times, and severe greenhouse gas emissions. To address this cyber-ecological vulnerability, we propose the Hybrid Multi-Agent State Estimation (H-MASE) protocol, a fully decentralized decision-support framework designed from an applied systems reliability engineering perspective. By deploying PSAs and VLAs directly onto IoT-enabled edge devices at smart intersections, H-MASE leverages a hop-by-hop edge computing topology to collaboratively track macroscopic route flow dynamics. Mathematically, this distributed estimation process is formulated as a network-wide least-squares convex optimization problem, where local projection operators function as exact Distributed Gradient Descent steps to minimize the global residual sum of squares. The distributed consensus mechanism acts as a spatial variance reduction tool, effectively dampening measurement noise and stochastic demand fluctuations. Furthermore, we introduce an autonomous anomaly detection logic that isolates severe structural faults rapidly, which is mathematically structured to prevent false alarms under bounded disturbance conditions. Numerical simulations demonstrate that the protocol yields a highly resilient optimality gap (e.g., a Root Mean Square Error of merely 0.81 vehicles per estimated state) even under catastrophic hardware failures. Ultimately, H-MASE provides a robust, fail-safe data foundation for sustainable urban logistics and green-wave signalization, ensuring that smart cities maintain ecological resilience and optimal resource utilization under severe structural disruptions. Full article

This paper presents a hardware-accelerated signal processing architecture for OFDM-based vehicular networks that integrates crypto-agile adaptive encryption on a Xilinx Kintex-7 FPGA. The encryption layer is tightly coupled to the OFDM modulation/demodulation pipeline, enabling secure real-time signal processing for V2X communications without disrupting the baseband chain. A context-aware pre-selection unit dynamically selects among hardware cipher primitives based on latency constraints, security requirements, and channel conditions. The current prototype implements and synthesizes AES-128 as the primary block cipher, while ASCON (NIST lightweight AEAD) and Keccak (SHA-3 foundation) are validated through RTL simulation and architectural integration, demonstrating crypto-agility across block, AEAD, and sponge-based primitives. DES is retained solely as a legacy reference for backward-compatibility evaluation and is not recommended for secure V2X deployment. The design adopts a modular decoupling strategy in which cryptographic engines interface with a unified buffering and interleaving subsystem, enabling hardware-based single-cycle cipher switching without partial reconfiguration. FPGA results demonstrate sub-microsecond cryptographic processing latencies with moderate resource utilization, preserving the timing budget of latency-sensitive vehicular services. AES-128 provides standard-strength encryption, while ASCON and Keccak offer lightweight and sponge-based alternatives suited to constrained IoV platforms. Specifically, the implemented AES-128 core achieves a throughput of 1.02 Gbps with a switching latency of 86 ns, verified across 10 randomized transitions with a 99.99% success rate and zero data corruption. The ASCON and Keccak cores attain throughput-to-area efficiencies of 2.01 and 1.47 Mbps/LUT, respectively, at a unified clock frequency of 50 MHz. All acronyms are defined at first use and a complete list of abbreviations is provided prior to the reference section. Full article

Inertial measurement units (IMUs) in low-cost navigation systems suffer from significant drift and noise errors due to sensor biases, scale factor instability, and nonlinear stochastic noise. This paper proposes a hybrid error compensation approach that combines Fast Orthogonal Search (FOS), a nonlinear system identification technique, with deep Long Short-Term Memory (LSTM) neural networks to improve IMU signal accuracy in GNSS-denied navigation. The FOS algorithm efficiently models deterministic error patterns (such as bias drift and scale factor errors) using a small training dataset, while the LSTM learns the IMU’s complex time-dependent error dynamics from much longer training data. In the proposed method, FOS is first used to predict the output of a high-end IMU based on that of a low-end IMU, and the trained FOS model is then used to extend the training data for an LSTM-based predictor. We demonstrate the efficacy of this FOS–LSTM hybrid on real vehicular IMU data by training with a limited segment of high-precision reference measurements and testing on extended operation periods. The hybrid model achieves high predictive accuracy for predicting the high-end signal based on the low-end signal, with a mean squared error below 0.1% and yields more stable velocity estimates than models using FOS or LSTM alone. Although long-term position drift is not fully eliminated, the proposed method significantly reduces short-term uncertainty in the inertial solution. These results highlight a promising synergy between model-based system identification and data-driven learning for sensor error calibration in navigation systems. Key contributions include FOS-based pseudo-label bootstrapping for data-efficient LSTM training and a navigation-level evaluation illustrating how signal correction impacts dead reckoning drift. Full article

This paper investigates M-PSK symbol detection in Orthogonal Frequency Division Multiplexing (OFDM) systems for wideband Vehicle-to-Vehicle (V2V) communications using lightweight convolutional neural networks. In doubly dispersive channels, Inter-Carrier Interference (ICI) degrades subcarrier orthogonality, rendering conventional equalization ineffective. Current ICI mitigation techniques face a trade-off between Bit-Error Rate (BER) performance and computational complexity, limiting their applicability in dynamic vehicular scenarios. To address this issue, a low-complexity MobileNetV3-based receiver is proposed, incorporating a signal-model-driven preprocessing stage that compensates for Doppler-induced phase distortions responsible for ICI. Simulation results show that the proposed receiver improves BER performance compared to conventional equalizers and recent neural-based schemes in the low-SNR regime (below 15 dB) while maintaining computational complexity close to linear least-squares detection. Full article

Ensuring fast, reliable communication for emergency vehicles is vital in a smart-city vehicular ad hoc network. However, conventional technologies such as dedicated short-range communications and radio links often fail to meet strict low-latency, high-reliability requirements in congested, resource-limited environments. We developed a priority-based power allocation scheme that reserves sufficient transmission power and bandwidth for emergency vehicles while maintaining acceptable service for regular vehicles. Simulation and performance analysis show that the proposed method achieves lower outage probability and higher sum rate than existing resource allocation strategies under various channel conditions and signal-to-noise ratios, providing an effective communication solution for urban emergency services. Full article

The evolution of next-generation urban infrastructure necessitates the deployment of intelligent pavement systems capable of real-time data acquisition, adaptive response, and predictive analytics. This article presents the design, implementation, and performance evaluation of the smart pavement system incorporating multimodal embedded sensors for traffic density analysis, structural health monitoring, and environmental surveillance. SPS integrates piezoelectric transducers, micro-electro-mechanical system accelerometers, inductive loop coils, fiber Bragg grating (FBG) sensors, and capacitive moisture and temperature sensors within the asphalt and sub-base layers, forming a distributed sensor network that interfaces with an edge-AI-enabled data acquisition and control module. Each sensor node performs localized pre-processing using low-power microcontrollers and transmits spatiotemporal data to a centralized IoT gateway over an adaptive mesh topology via long-range wide-area network or 5G-Vehicle-to-Everything protocols. Data fusion algorithms employing Kalman filters, sensor drift compensation models, and deep convolutional recurrent neural networks enable accurate classification of vehicular loads, traffic, and anomaly detection. Additionally, the system supports real-time air pollutant detection (e.g., NO₂, CO, and PM2.5) using embedded electrochemical and optical gas sensors linked to mobile roadside units. Field deployments on a 1.2 km highway testbed demonstrate the system’s capability to achieve 95.7% classification accuracy for vehicle type recognition, ±1.5 mm resolution in rut depth measurement, and ±0.2 °C thermal sensitivity across dynamic weather conditions. Predictive analytics driven by long short-term memory networks yield a 21.4% improvement in maintenance planning accuracy, significantly reducing unplanned downtimes and repair costs. The architecture also supports vehicle-to-infrastructure feedback loops for adaptive traffic signal control and incident response. The proposed SPS architecture demonstrates a scalable and resilient framework for cyber-physical infrastructure, paving the way for smart cities that are responsive, efficient, and sustainable. Full article

Autonomous vehicles (AVs) are transforming transportation by improving safety, efficiency, and intelligence through integrated sensing, computing, and communication technologies. However, their growing reliance on Vehicle-to-Everything (V2X) communication exposes them to cybersecurity vulnerabilities, particularly at the physical layer. Among these, jamming attacks represent a critical threat by disrupting wireless channels and compromising message delivery, severely impacting vehicle coordination and safety. This work investigates the robustness of New Radio (NR)-V2X-enabled vehicular systems under jamming conditions through a dual-methodology approach. First, two Cooperative Intelligent Transport System (C-ITS) scenarios standardized by 3GPP—Do Not Pass Warning (DNPW) and Intersection Movement Assist (IMA)—are implemented in the OMNeT++ simulation environment using Simu5G, Veins, and SUMO. The simulations incorporate four types of jamming strategies and evaluate their impact on key metrics such as packet loss, signal quality, inter-vehicle spacing, and collision risk. Second, a complementary laboratory experiment is conducted using AnaPico vector signal generators (a Keysight Technologies brand) and an Anritsu multi-channel spectrum receiver, replicating controlled wireless conditions to validate the degradation effects observed in the simulation. The findings reveal that jamming severely undermines communication reliability in NR-V2X systems, both in simulation and in practice. These findings highlight the urgent need for resilient NR-V2X protocols and countermeasures to ensure the integrity of cooperative autonomous systems in adversarial environments. Full article

This work presents a complete multi-serial adaptive bus interface system compatible with the most widely used industrial serial communications standards: RS-232, RS-485, RS-422, and CAN. The proposed system automatically detects the connected serial interface type through analog line sensors and dynamically redirects the bus to the appropriate transceiver using a logical multiplexer. This approach aims to simplify the configuration of heterogeneous serial devices in complex and modular integration scenarios, such as body builders in industrial or vehicular systems. The hardware is designed as a scalable PCIe card-based device, allowing multiple adaptive bus interfaces to be integrated within a rack-based modular architecture. In addition, a single 5-pin plug-and-play connector is proposed by unifying the different bus signals of the transceivers, thereby simplifying cabling and deployment. Complementary implemented capabilities include baud rate auto-detection and supervision, as well as automatic direction-control functionality for RS-485 communication. Experimental validation demonstrated that the proposed system successfully detected and redirected all supported interfaces, achieving reliable connection and disconnection within an average time of 2.5 s. Furthermore, the integrated baud rate auto-detection algorithm accurately identified transmission speeds up to 1 Mbps in under 80 ms, while the automatic direction-control capability operated reliably at speeds up to 576,000 bps. Full article

Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated navigation is one of the key methods for achieving precise positioning in complex urban environments. However, in some scenarios such as urban canyons, overpasses, and foliage occlusion, GNSS signals are frequently attenuated or interrupted, leading to degraded positioning accuracy when relying solely on INSs. To address this limitation, this study developed an improved GNSS/INS-integrated navigation algorithm based on a hybrid framework that combines a Robust Adaptive Kalman Filter (RAKF) with a Radial Basis Function (RBF) neural network. The RAKF allows a multi-criterion optimization strategy to be created to adaptively adjust the measurement noise covariance matrix according to GNSS data quality indicators such as PDOP, the number of satellites, and signal quality factors. This enhances the filter’s robustness and outlier detection capability under degraded GNSS conditions. Meanwhile, the RBF network is trained to predict pseudo-position increments, which substitute missing GNSS measurements during signal outages to maintain continuous navigation. Real-world vehicular experiments were conducted to evaluate the proposed RBF-aided RAKF (RBF-RAKF) against three other methods: the Extended Kalman Filter (EKF), standard RAKF, and RBF-aided Kalman Filter (RBF-KF). The experimental results demonstrate that during GNSS outages the proposed method achieved root mean square (RMS) positioning errors of 0.94, 1.02, and 0.21 m in the north, east, and down directions, respectively, representing improvements of over 90% compared with conventional filters. Moreover, the algorithm maintained meter-level horizontal accuracy and sub-meter vertical precision under severe GNSS signal degradation. These results confirm that the proposed RBF-RAKF algorithm provides stable and high-precision navigation performance in challenging urban environments. Full article