Search Results (2,592)

Automotive Ethernet networks increasingly rely on SOME/IP within the AUTOSAR Adaptive Platform for service-oriented ECU communication, yet SOME/IP lacks built-in security, exposing systems to Man-in-the-Middle (MitM), replay, spoofing, and eavesdropping attacks. As of AUTOSAR Release R22-11, no standardised security profile addresses both authenticity and confidentiality for multicast SOME/IP traffic. This paper proposes a hybrid security architecture combining SecOC (CMAC-AES256, instantiated as a vendor-defined security profile) applied to the SOME/IP application Protocol Data Unit (above OSI Layer 4) with IEEE 802.1AE MACsec at the data link layer (Layer 2): SecOC provides end-to-end message authentication and replay protection, while MACsec adds hop-by-hop confidentiality and integrity, which coincides with end-to-end confidentiality only on a single-hop path. The SecOC and MACsec protocol constructions themselves are not modified; only SecOC’s parameter profile deviates from the three predefined profiles. The architecture is evaluated across ten sub-scenarios (five configurations, each tested under unicast and multicast communication; 500 iterations recorded per sub-scenario). For the Hybrid-PSK multicast configuration (Scenario 3a-B), steady-state end-to-end latency adds $27.8$ ms ($33.89$ ms versus the $6.12$ ms unsecured-multicast baseline, Scenario 1-B; both figures are measured on the identical two-VM topology using the same half-round-trip method); the worst-case measured 99th percentile of this absolute latency, across all hybrid configurations, is $40.99$ ms (Hybrid-RSA multicast, Scenario 3b-B). Of the added overhead, MACsec contributes 11.5% ($3.18$ ms), isolated by re-running the SecOC-only scenarios on the same two-VM topology used for the hybrid scenarios, and one-time RSA-2048 session establishment adds $25.2$ ms to $33.8$ ms at startup. Cross-referencing the SecOC path cost against CPU utilisation indicates that most of it is user-space integration overhead rather than cipher execution, a split not isolated by direct microbenchmark. The reported MACsec figure remains an upper bound in one respect: it reflects the Linux kernel software MACsec driver rather than a PHY-offloaded implementation. At this single quiet operating point (64-byte payload, approximately 20 messages/s, two receivers, no background load), the measured worst-case p99 leaves an approximate 9 ms margin against an assumed 50 ms soft real-time budget for ADAS and infotainment messaging; whether this margin survives realistic ECU loading, larger receiver groups, and production-stack constant factors was not evaluated. This margin is also bound to the present, unoptimised software path: because most of the SecOC path cost is user-space integration overhead rather than cipher execution, removing it is projected to widen the margin substantially, so the reported headroom characterises this prototype rather than a ceiling on the architecture. Full article

In multi-hop UAV ad hoc networks employing the Statistical Priority-based Multiple Access (SPMA) protocol, the HELLO broadcast interval cannot be arbitrarily shortened due to the inherent upper bound on the per-slot transmission probability of each node in saturated networks, which fundamentally limits the estimation accuracy of Channel Occupancy Statistics (COS). To address this problem, this paper proposes a spatial correlation-aided multi-source asynchronous Kalman filtering method, abbreviated as SMA-KF. On the basis of conventional COS broadcasting, SMA-KF introduces two complementary observation sources: COS measurements piggybacked on data packets, and spatially correlated observations from common neighbors compensated by historical biases. These three types of observations are integrated into a unified Kalman filtering framework, and a state-space model suitable for asynchronous intermittent observations is constructed. Theoretical analysis verifies the convergence of the algorithm. Simulation results demonstrate that the proposed algorithm significantly outperforms the EWMA and TW algorithms across all test scenarios, and achieves overall lower error than BiLSTM. Under the extremely sparse observation condition with a HELLO broadcast interval of 600 slots, the Normalized Root Mean Square Error (NRMSE) of SMA-KF is 28.28%, which is 32.8% and 26.2% lower than those of EWMA (42.11%) and TW (38.33%), respectively. In the heavy-traffic scenario with an average data packet arrival interval of 20 slots, the NRMSE of SMA-KF is as low as 4.80%, whereas those of EWMA and TW are 17.49% and 15.40%, respectively, corresponding to reductions of 72.6% and 68.8%. In comparison with BiLSTM, SMA-KF achieves lower NRMSE in five out of seven traffic configurations, while BiLSTM exhibits only marginal and statistically insignificant advantages in the remaining two configurations. Link interruption experiments show that SMA-KF maintains NRMSE between 5.68% and 10.40% across the entire meaningful interruption coverage range of 0% to 53%, consistently outperforming all benchmark algorithms. Moreover, SMA-KF consistently achieves the lowest estimation error under varying node mobility speeds. Parameter sensitivity analysis confirms that SMA-KF maintains stable performance across a wide range of parameter values. These results validate the effectiveness of multi-source observation fusion and spatial cooperative estimation in improving both the accuracy and robustness of COS estimation. Full article

Accurate radar tracking of highly maneuverable unmanned aerial vehicles (UAVs) is a key enabling technology for low-altitude airspace surveillance, counter-UAS defense, and UAS traffic management (UTM). Once a non-cooperative UAV has been detected, estimating its motion state must cope with nonlinear polar-coordinate observations, unknown maneuver-mode switching, and multi-scale state variations driven by agile drone flight, making it difficult for classical IMM/UKF filters and deep sequence models to preserve local maneuver response and long-term temporal consistency. We propose MSGate, a multi-scale gated temporal network organized along an “observation-representation-fusion-constraint” pipeline. A non-learnable physical front end maps polar measurements into a Cartesian observation trajectory of the same dimension as the UAV state. Multi-scale gated convolution and RoPE-Transformer encoding extract local maneuver responses and long-range dependencies. A shared gated dual-path decoder fuses the two paths adaptively at each time step and channel, and velocity-smoothness and position-velocity kinematic consistency terms regularize the predicted trajectory. On the real-UAV datasets UZH-FPV, EuRoC MAV, and NeuroBEM, under a unified range-azimuth observation protocol, MSGate attains the lowest average position and velocity errors (Pos-RMSE $0.0486\mathrm{m}$; Vel-RMSE $0.1767\mathrm{m}/\mathrm{s}$), outperforming the strongest time-series baseline TimeMixer, and generalizes to a separate nano-quadrotor dataset (NanoBench). MSGate provides an accurate, maneuver-robust solution for radar state estimation of highly maneuverable UAVs. Full article

Inventory and condition assessment of vertical traffic signs constitutes an essential activity for road safety management, as these signs serve as the primary mechanism for regulating vehicular flow and alerting drivers to prevailing road conditions. However, current inspection procedures in Peru rely on field crews that evaluate each sign manually, thereby constraining the frequency, objectivity, and scalability of the process. This paper presents SISEVIR (Sistema de Supervisión de Señales Verticales en Infraestructura Vial), a three-stage deep learning pipeline for the automated diagnosis of vertical traffic sign condition. The first stage employs YOLO26s-seg for instance segmentation of 31 sign classes, achieving a test mAP50 of 0.9305 (box) and 0.9224 (mask). The second stage classifies each detected sign into seven deterioration states using EfficientNet-B0, optimized through a five-experiment ablation study that identified progressive offline augmentation as the most effective strategy for handling a 147:1 class imbalance (macro F1 = 0.8252; pairwise McNemar’s tests with Holm–Bonferroni correction did not confirm significance at the family-wise $\alpha =0.05$ level). The third stage integrates Qwen2-VL-2B-Instruct, a vision–language model, to generate natural-language descriptions of sign condition aligned with the MTC Manual of Traffic Control Devices for Streets and Highways. A structured evaluation by two independent raters on 35 descriptions yielded a correctness rate of 93.5% among valid responses (95% CI: 79.3–98.2%, Cohen’s $\kappa =1.00$). The system was trained and validated on a proprietary dataset of 5935 images and 6412 labeled crops collected along three routes in the Ayacucho Region (246.6 km total), with an inter-rater reliability of $\kappa =0.802$ (95% CI: 0.676–0.928). SISEVIR processes vehicular video at 30.7 FPS on an NVIDIA RTX 5080 GPU and assigns each sign a level within a four-tier condition scale (Optimal through Critical) linked to specific maintenance interventions, significantly reducing the time, cost, and personnel required compared with the manual inspection method established in the MSV-2016 Road Safety Manual. Full article

Modern smart agriculture increasingly relies on edge computing for real-time, high-concurrency tasks such as wide-area drone-based crop monitoring. However, highly volatile workloads and severe environmental noise in agricultural Internet of Things (IoT) networks often lead to resource congestion and high latency when relying on traditional reactive scheduling. To address these challenges, this paper proposes a hybrid prediction-driven proactive resource scheduling method for edge computing. We construct a Variational Mode Decomposition-Convolutional Neural Network-Attention-Bidirectional Long Short-Term Memory (VMD-CNN-Attention-BiLSTM) model to filter environmental noise and accurately capture the spatio-temporal features of bursty traffic. Furthermore, a deep reinforcement learning scheduling algorithm based on Proximal Policy Optimization (PPO) incorporates future workload trends into its state space, dynamically optimizing task offloading. To evaluate the proposed Predictive Computational Scheduling Framework (PCSF), we developed a custom edge computing simulation environment and synthesized a hybrid dataset combining real-world server logs from the Alibaba Cluster Trace with deep learning inference workloads derived from a Wheat Plant Diseases image repository. Simulations demonstrate that the prediction model achieves a Root Mean Square Error of 0.030 and a Mean Absolute Error of 0.0215. Compared to static and reactive baselines, the PCSF reduces average task timeout violations to 2.2 and total system energy consumption by nearly 40%. This proactive mechanism effectively overcomes decision-making lags, enabling efficient, low-latency computing resource allocation for modern agricultural facilities. Full article

Vehicular accidents are among the leading causes of fatalities, economic losses, and traffic disturbances worldwide. Understanding the intricacies of accident emergence and propagation is crucial for devising mitigation strategies. Among the different analysis approaches, cellular automata modeling stands out as a powerful tool since it enables the reproduction of complex collective dynamics through simple local interactions. Although numerous CA-based studies have addressed traffic accidents under different conditions, the literature still lacks a comprehensive synthesis dedicated to cellular automata-based vehicle-to-vehicle accident modeling. This systematic review classifies and comparatively analyzes how cellular automata models represent vehicle-to-vehicle collision mechanisms, dangerous traffic states, accident occurrence, and accident-related traffic effects. Through searches in Scopus, Web of Science, TRID, and IEEE Xplore, 740 records were identified and subsequently filtered to 90 papers according to the PRISMA methodology. The selected studies were classified according to a common taxonomy that includes rear-end, head-on, and side-impact collisions, as well as lateral conflicts. The contribution of the proposed taxonomy is threefold: to provide a deeper understanding of the key modeling principles, to review the types of accidents most frequently analyzed, and to identify current research gaps. Overall, this paper serves as a reference point for developing, comparing, and extending cellular automata models for traffic safety analysis. Full article

This study investigates how coordinated wireless and fast charging services reshape electric vehicle departure time and path–charging choices when a trip-level charging requirement must be completed before arrival. A multi-class dynamic user equilibrium model is formulated for road networks containing wireless charging lanes and fast charging stations. An energy-aware dynamic network loading model propagates traffic and battery states, transfers upstream wireless energy into the residual station workload, and determines endogenous waiting. The equilibrium is expressed as a finite-dimensional variational inequality and solved by an energy-aware inertial fixed-point framework with safeguarded route swapping and independent verification. Experiments on the Nguyen–Dupuis and Sioux Falls networks show that low-state-of-charge users depart 6.91 min earlier on average, while exposure-informed wireless-charging placement can substantially reduce downstream station waiting and exhibits saturation once all behaviorally exposed links are active. Under compound demand and low-state-of-charge pressure, roadway queues activate more sharply than station waiting. In a common Sioux Falls algorithm benchmark, the inertial method reaches stable acceptance in 776.2 s compared with 1562.9 s for its non-inertial counterpart. The method of successive averages crosses the practical gap threshold earlier but does not satisfy the common flow-stability criterion within 3000 updates and 9018.1 s. Across 30 final Sioux Falls scenarios, all solutions satisfy the practical verified gap and physical feasibility gates, with 11 difficult cases requiring explicit route-swap continuation. The results clarify the complementary operational roles of corridor and station charging while delimiting the numerical and behavioral assumptions of the framework. Full article

Traditional traffic target detection heavily relies on manual processing. However, the latest advancements in deep learning have significantly enhanced the capabilities of target detection and multi-target tracking. To address these challenges, this paper proposes a perception-tracking-reasoning framework based on traffic rules, which is used for vehicle recognition and driving-state analysis in surveillance videos. This framework integrates enhanced vehicle perception, cross-frame identity association, trajectory-state modeling, and interpretable rule reasoning into a unified processing flow. Finally, experiments show that the main advantage of the proposed model lies in its ability to detect small-sized vehicle targets and improve trajectory stability in complex traffic scenarios. Full article

Driving-fatigue-induced attentional decline and response retardation are critical contributors to traffic accidents. However, stably and precisely identifying fatigue states from noisy electroencephalogram (EEG) signals remains a challenging issue in intelligent driving safety. To address the dual deficiencies of traditional methods in fatigue feature extraction precision and noise robustness, this paper innovatively constructs a collaborative recognition framework that integrates an Improved Strip Attention Mechanism (ISAM) with a Deep Residual Shrinkage Network (DRSN). The core innovations of this framework are twofold: ISAM achieves precise localization and focused enhancement of fatigue-related rhythmic bands in EEG signals via row–column separable adaptive pooling and channel-wise attention augmentation; concurrently, the DRSN module introduces an improved soft-thresholding function, which adaptively generates filtering thresholds through channel attention to effectively suppress noise and artifact interference in physiological signals. The deep fusion of these two modules forms a closed-loop optimization chain of “targeted feature reinforcement–adaptive noise suppression,” enabling the model to stably extract highly discriminative fatigue representations from complex non-stationary EEG signals. Validation on two public datasets, SEED-VIG and SADT, demonstrates that the proposed method achieves recognition accuracies of 98.86% and 97.38%, respectively, outperforming mainstream methods such as the convolutional spatial-frequency network and multi-scale convolutional neural network by 17.38% and 17.76%. These results confirm the significant advantages of the proposed dual-module collaborative architecture in precise fatigue characterization and anti-interference capability, offering a highly reliable technical solution for real-time driver mental fatigue monitoring in real-world road scenarios. Full article

Traffic reallocation is usually framed as moving flights toward apparent spare airport capacity, yet the same move can redirect demand through different runways and shared corridors. We tested whether the local capacity response to a fixed reallocation remains invariant to this resource-chain composition. Using a discrete-event model of the Beijing Capital, Beijing Daxing and Tianjin Binhai airports, we crossed seven airport allocations with five prespecified composition levels. At a model-success threshold of 0.90, increasing Beijing Capital’s share by five percentage points produced a finite-search response of $[-145,-115]$ flights under the lower-pressure composition but $[10,25]$ under the higher-pressure composition. The propagated interaction interval was $[125,170]$ flights. The reversal was consistent across three independent seed families, and 95% paired-bootstrap percentile intervals excluded zero for both outer compositions. None of 100 prespecified pressure-label controls met the certain-tail criterion, while seven control intervals overlapped the observed band. These are reference-tail proportions, not a randomization p-value. Runway and corridor relief each restored all seven failing operating-state boundary cases. These results show that reallocation acts on a coupled airport–resource system whose response depends on the complete chains carried by demand. These findings are model-conditional finite-search results under expert-bounded perturbations, not an official capacity determination or a calibrated estimate of operational reliability. Full article

This study reports a combined morphological, surface chemical, and mineralogical characterization of fine particulate matter (PM_2.5) collected at three coastal sites—Northern (Abhour), Middle (Alhamraa), and Southern (Alkhomra)—in Jeddah, Saudi Arabia, during the summer (15 June–15 September 2017). The work complements a companion trace-element study of the same campaign by adding scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS/EDX mapping), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD), thereby linking bulk concentrations to particle morphology, surface oxidation state, and crystalline phase. Mean PM_2.5 concentrations were 22.2, 18.9, and 14.2 µg m⁻³ at the North, Middle, and South sites, respectively. Because samples were collected on borosilicate glass-fibre filters, the SEM images are dominated by the intrinsic fibrous matrix of the substrate; the collected aerosol is resolved as discrete sub-micrometre particles and agglomerates decorating the fibres, and the morphological interpretation is framed accordingly. XPS confirmed that surface metals (Fe, Al, Ca, and traces of Pb, Cu, Zn) occur predominantly in oxidized states, with the Middle urban site showing the strongest Fe and Pb signals. XRD identified quartz, calcite, gypsum, hematite/magnetite, and aluminum oxides, with additional Pb and Cu phases at the Middle and South sites. Principal component analysis (PCA) resolved four sources—mixed marine–crustal, terrigenous/industrial (Fe–Ti–Mn), oil combustion and shipping (V–Ni–Cu), and combustion/legacy-traffic (Pb–Zn)—consistent with prior Jeddah and Red Sea studies. The integrated approach provides surface-speciation and mineralogical details not available from bulk elemental analysis alone and establishes baseline information relevant to source management and health-risk assessment in arid coastal cities. Full article

Internet of Vehicles (IoV) security mechanisms often classify isolated messages or assign node-level trust scores, yet these decisions do not explain whether a malicious but authenticated event has distorted the intended evolution of traffic. This paper proposes IntentProv-IoV, a causally grounded provenance framework for traffic-intent preservation in V2X environments. Traffic intent is modeled as the short-horizon collective state expected under non-adversarial conditions, and deviation is measured between predicted and observed traffic states. The framework constructs temporal provenance graphs linking vehicles, roadside units (RSUs), cooperative perception outputs, prediction nodes, and traffic-control decisions. To remove the ambiguity of marginal contribution, node contribution is formalized as an interventional effect in a structural causal model and estimated through Monte Carlo counterfactual edge-weight attenuation, with a linear sensitivity fallback for real-time edge deployment. A calibrated composite score integrates anomaly evidence, traffic-intent deviation, trust risk, and provenance contribution. The evaluation design compares IntentProv-IoV with detection, trust, blockchain trust, graph anomaly, Granger causal, structural causal, and counterfactual GNN baselines and includes predictor sensitivity, adaptive adversaries, prediction noise, packet loss, trajectory-only real-data validation, and edge overhead. Simulation-scale results indicate improved attribution precision, stronger traffic-intent deviation reduction, and edge-suitable latency. By shifting V2X security from message-level detection to causally explainable traffic-intent assurance, IntentProv-IoV provides a more accountable security objective for cooperative vehicular systems. Full article

Accurate prediction of electric vehicle charging demand is imperative for ensuring grid stability and optimizing urban mobility resources. While the emergence of large language models has introduced translation-based forecasting paradigms, existing methods typically suffer from numerical precision loss due to textual tokenization and fail to capture complex, non-Euclidean spatial dependencies. To address these limitations, this study introduces STAR, a spatio-temporal agentic reasoning framework that fundamentally redefines the forecasting task as a generative reasoning process. STAR integrates three core innovations, beginning with a temporal patching alignment mechanism that projects historical time-series segments into dense semantic vectors to preserve numerical fidelity. This is seamlessly combined with a graph-conditioned spatial context fusion module that empowers the agent to retrieve dynamic spatial dependencies via cross-attention-based topological fusion over an urban knowledge graph, thereby linking temporal dynamics with spatial causality. Finally, the framework employs an agentic chain-of-thought inference engine that mandates the generation of explicit reasoning traces by analyzing trends and synthesizing external factors prior to outputting the final forecast. Extensive experiments on ST-EVCDP, an open benchmark dataset collected from Shenzhen for urban EV charging demand prediction, demonstrate that STAR significantly outperforms state-of-the-art baselines, achieving a 27.3% to 41.9% prediction improvement for 60 min horizons compared to existing methods. Furthermore, the framework exhibits exceptional zero-shot cross-zone transferability across unseen traffic districts, providing interpretable decision support for critical infrastructure management. Full article

Frame aggregation is central to modern IEEE 802.11 networks, yet the existing performance models fail to capture how it behaves under usual unsaturated traffic. Some rely on a predefined service-time distribution; others cover only narrow unsaturated cases, such as stations withholding transmission until K packets accumulate or stations being modeled as if they always have a packet queued. This paper develops a performance model for 802.11 networks with frame aggregation under unsaturated traffic in which the aggregation size and service time emerge dynamically from the offered traffic load, the random backoff process, and the number of stations rather than from any of these simplifying assumptions. Beyond throughput, the model derives closed-form estimates of the average aggregation size, service time, and per-station queue length directly from the steady-state distribution of a three-dimensional Markov chain. Performance evaluations across two physical-layer rates (867 and 150 Mbps), two queue capacities, and different numbers of stations show that the proposed model produces throughput and aggregation-size estimates that closely match an event-driven simulator, while the service time and queue-length estimates reflect the model’s own assumption. Full article

Internet of Things(IoT)/edge intrusion-detection systems operate on distributed traffic and system-state data whose distributions vary across gateways, services, and attack conditions. We study a server-assisted federated setting in which a teacher reference is fitted from a permitted server-accessible training pool and explicitly distinguish this simulation assumption from fully decentralized deployment. The proposed framework evaluates progressive local training through boundary stabilization, confidence-weighted decision distillation, representation alignment, and validation-quality-aware aggregation. The evaluation uses a leakage-controlled protocol: server and client validation subsets are held out before federated training, update quality and early stopping use validation data only, and the final-test split is evaluated once. Results on NSL-KDD, CIC-IDS2017, Edge-IIoTset, and the ToN-IoT network dataset show competitive primary performance and stronger robustness in several severe label-skew settings. On the Telemetry of Things(ToN-IoT) with Dirichlet alpha = 0.1, the proposed method achieves 91.46 ± 5.54 F1, compared with 53.73 ± 49.00 for FedAvg and 53.77 ± 48.92 for FedProx. The results do not establish universal superiority or a universally optimal stage order: competing methods remain stronger in selected stable and attack-shift settings. The framework is therefore presented as a bounded, server-assisted robustness-oriented training strategy for heterogeneous IoT/edge intrusion detection. Full article