Search Results (952)

Intelligent sensor networks are increasingly deployed in security-critical environments, where confidential sensing data may be forwarded through low-security relay sensors that are vulnerable to compromise, misuse, or internal eavesdropping. This paper investigates attack-aware secure sensor transmission in a sensor network enabled by a pinching-antenna system (PASS) with a potentially compromised relay sensor. The relay sensor assists data forwarding from a source sensor to a destination fusion center, but its received signal may also cause internal information leakage. To mitigate this threat, a trust-aware cooperative jamming framework is proposed, in which a cooperative security node generates artificial noise, feeds it into the PASS waveguide, and radiates it through selected pinching antennas toward the untrusted relay while limiting interference leakage to the legitimate fusion center. A trust-aware cooperation coefficient is introduced to characterize the security-response capability of the cooperative security node, and a candidate-set-aided PASS activation and jamming-power allocation algorithm is developed for secure sensing-data delivery. Reliable-and-secure probability, secrecy outage probability, and attack-aware secure delivery probability are adopted to jointly evaluate data reliability, confidentiality, and relay-attack resilience. Numerical results show that the proposed scheme improves secure transmission performance compared with fixed-antenna jamming, random PASS activation, trust-unaware PASS, and no-jamming benchmarks. The results also indicate that a detection-triggered PASS response can maintain a higher secure delivery probability as the relay attack probability increases. Full article

Collaborative intelligent sensing in the Social Internet of Things (Social IoT) relies on distributed AI-enabled sensors to support complementary information sharing, multimodal perception, and real-time autonomous decision-making. Under high-load conditions, mismatches between resource provisioning and sensing quality of experience (QoE) can significantly degrade system performance in applications such as smart cities. To address this issue, this paper proposes a service priority-aware collaborative sensing support framework based on a joint next-generation passive optical network (NG-PON) and cooperative intelligent service-based radio access network (CIS-RAN) architecture. The framework enables edge AI-driven inference and distributed sensor collaboration in heterogeneous Social IoT environments. Service-slice-specific priority weights are assigned to optical network units (ONUs) and wavelengths according to the QoE requirements and latency sensitivity of sensing tasks, allowing dynamic wavelength tuning that prioritizes high-impact collaborative services. The utility of a centralized intelligent processing pool is formulated to achieve priority-consistent and efficient resource coordination under collaborative constraints. In addition, a multi-agent AI-driven optimization framework is employed to derive adaptive resource allocation strategies that incorporate service priorities while satisfying stringent service-level agreements (SLAs). Simulation results show that the proposed framework improves system-level proxy metrics, including total utility, wavelength satisfaction, and resource utilization, compared with representative baseline schemes. Full article

Ultra-wideband (UWB) inter-node ranging provides relative-distance constraints for cooperative localization in GNSS-denied UAV swarms, but dense candidate networks can exceed the available ranging slots, communication bandwidth, computation, and energy. This paper proposes a Fisher-information-based cooperative sensor node pre-selection method under heterogeneous ranging noise. All mobile nodes remain in the localization state, while the selected nodes induce the active ranging-link set. Selected-node, induced-link, ranging-slot, and normalized general-resource budgets are represented separately. Using predicted geometry and estimated link-quality weights, a gauge-free normalized Fisher information matrix combines link geometry, link-quality-dependent weights, and topology-induced coupling. A trace-based generalized GDOP (G-GDOP) criterion is optimized by a two-stage greedy heuristic with recursive matrix updates. The experiments show that G-GDOP is a local observability and information-quality metric rather than a direct predictor of topology-level nonlinear recovery error. Within the same topology, normalized local RMSE increased from 0.698 [0.673, 0.752] in the Low G-GDOP group to 1.014 [0.999, 1.068] and 1.980 [1.806, 2.180] in the Medium and High groups. Increasing the selected-node budget from K = 6 to K = 20 reduced median RMSE from 0.550 to 0.148 m while increasing the median induced-link number from 109 to 214. Additional tests covered Gaussian and heterogeneous ranging noise, deterministic NLOS bias, online link-weight errors, and predicted-position uncertainty. Direct Inversion and Woodbury Updating were numerically equivalent within a predefined tolerance in all 24 size–regime combinations, and a Woodbury runtime advantage was supported in 20 conditions. The proposed framework therefore provides an interpretable resource-aware pre-selection module without implying an unconditional real-time guarantee. Full article

Orchard production involves intensive labor requirements, limited operational periods, and highly dynamic and complex working environments. Consequently, the development of intelligent orchard robots has become a important approach to enhancing production efficiency and reducing reliance on manual operations. This review focuses on the demands of autonomous robotic systems operating in challenging orchard scenarios and provides a comprehensive overview of key technologies, including environmental perception and semantic cognition, autonomous navigation and environmental modeling, intelligent task execution, and collaborative robotic systems. Recent advances in fruit and blossom detection, branch and canopy structure perception, multi-modal sensor fusion for localization, semantic mapping, robotic harvesting control, variable-rate spraying, precision pollination, and autonomous intra-row weed management are systematically discussed. Furthermore, emerging technologies such as multi-robot coordination, robot–UAV cooperation, large language models (LLMs), and vision-language models (VLMs) for enhancing decision-making capabilities in agricultural robotics are reviewed. Finally, the existing challenges of orchard robots in terms of perception reliability, long-term autonomous navigation, operational robustness, system-level integration, and standardized performance evaluation are analyzed, followed by discussions on potential future research directions. Full article

Reliable navigation in GPS-denied environments remains a critical challenge for autonomous vehicles (AVs), particularly in complex indoor and urban settings. GPS-based localization systems often fail under these conditions, highlighting the need for resilient multimodal solutions. In this article, we present a radar-assisted tracking system that integrates LiDAR and inertial measurements within a sensor-fusion architecture to achieve robust navigation. The principal methodological contribution is a unified tracking and prediction framework that combines Bayesian state estimation with learning-based temporal prediction, enabling accurate tracking while continuously forecasting the slave robot’s short-term future state from mapping observations generated by the master robot, with a typical end-to-end perception-to-action latency of 20–60 ms. The communication and prediction forecasting module operates with an update interval below 35 ms, enabling real-time cooperative robotic operation. Sensor data are fused through a pipeline incorporating Gaussian Mixture Models (GMMs) for post-processing, which helps mitigate the limitations associated with individual sensors during edge processing. Moreover, Kalman filtering is employed to mitigate sensor noise and drift, thereby improving state estimation accuracy through trajectory smoothing. The fused spatiotemporal information is subsequently exploited by a Convolutional Recurrent Neural Network (CRNN) coupled with a Nonlinear Autoregressive model with eXogenous Inputs (NARX) to model the robot’s motion dynamics and provide short-horizon state prediction. Through simulations and real-world indoor experiments conducted in GPS-denied environments, we validate the system’s ability to provide accurate and continuous pose estimation with low localization errors. Experimental results show that the proposed framework achieves root-mean-square errors of 0.12 m, 0.15 m, and 0.28 m along the X, Y, and Z axes, respectively, while maintaining sub-meter maximum position deviations throughout the evaluated trajectories. These results confirm that the proposed framework provides reliable localization and predictive state estimation for cooperative robotic navigation in indoor GPS-denied environments. Future work will investigate outdoor validation and extend the framework to additional data-driven decision-making models for future robotic services. Full article

Agricultural machinery is rapidly developing toward electrification, intelligence, and autonomy, and the electric drive chassis has become a key technology for improving power transmission performance, operational efficiency, and energy utilization. This paper presents a review of research on electric drive chassis for agricultural machinery, focusing on four major aspects: electric drive systems, anti-slip and stability control of electric drive chassis, autonomous navigation system control technologies, and energy management strategies. The electric drive system is reviewed from the perspectives of drive motor technologies and drive architectures. Chassis control technologies are mainly discussed in terms of longitudinal anti-slip control and lateral stability control under complex terrain conditions. Autonomous navigation systems are summarized with respect to multi-source environmental perception, path planning, and path tracking control. Energy management strategies are classified into rule-based, optimization-based, and learning-based approaches according to their control principles, and the characteristics and applicable scenarios of each approach are analyzed. On this basis, the collaborative relationships among drive architecture, chassis control, autonomous navigation, and energy management are further discussed. Finally, future research directions are proposed, including highly integrated electric drive systems, vehicle-level collaborative control, multi-source sensor fusion, hybrid model-driven and data-driven control, global energy optimization, and multi-machine cooperative operation. This review provides a reference for the design and development of intelligent electric drive chassis for agricultural machinery. Full article

Structural health monitoring and regular damage inspection are critical to ensure the operational safety of civil infrastructure and reduce life-cycle maintenance costs, while traditional manual inspection suffers from low efficiency, high subjectivity, and occupational safety risks for inspectors in hard-to-reach areas. Although existing reviews have explored image-based damage detection, most focus on single damage types or individual infrastructure categories, with few providing quantitative bibliometric mapping of the whole field. This study combines bibliometric analysis and systematic review to trace the development trajectory, identify unresolved technical bottlenecks and industry–academia gaps, and provide a structured reference for researchers and engineering practitioners. Following PRISMA guidelines, 171 peer-reviewed publications from the Web of Science Core Collection (2009–2025) were included after two rounds of screening (initial retrieval: 892 records). CiteSpace and VOSviewer were jointly used to analyze publication trends, institutional cooperation networks, and emerging research hotspots, followed by a systematic review of technical evolution and engineering applications. Results show that annual publications have maintained a growth rate of over 40% since 2019, with China (54.4%) and the United States (22.2%) as the core global contributors; 89.5% of research outputs come from universities and research institutes, while enterprise participation accounts for only 8.3%, indicating a clear technology translation gap. Technically, the field has evolved from traditional digital image processing to deep learning paradigms (CNN, YOLO, U-Net, GAN, Transformer), integrated with UAV platforms and 3D reconstruction to achieve both intelligent damage identification and 3D quantitative assessment. Key bottlenecks include scarcity of high-quality multi-class annotated datasets, poor model robustness in complex field environments, insufficient pixel-to-engineering scale conversion accuracy, and low model interpretability. Future directions include multimodal sensor fusion, unsupervised domain adaptation for real-world generalization, lightweight edge-deployable detection models, strengthened industry–academia collaboration, and explainable artificial intelligence to accelerate technology deployment in engineering practice. Full article

This paper presents the latent model-joint embedding predictive architecture (LM-JEPA), a resource-efficient collaborative perception framework for connected and autonomous vehicles that integrates latent predictive representation learning with lightweight multi-modal reasoning. Autonomous driving in urban and highway environments requires accurate scene understanding under strict latency, energy, and communication constraints, limiting the practicality of large language model (LLM) and vision–language model (VLM)-based approaches in edge deployments. To address this, LM-JEPA encodes heterogeneous inputs including camera, LiDAR, radar, and map data into a unified latent space using a joint embedding predictive architecture, enabling efficient perception and reasoning without token-level inference. Unlike existing latent-space learning approaches that primarily learn predictive visual embeddings for single-modal perception, the proposed framework integrates multi-modal latent reasoning and adaptive sensor fusion to support collaborative perception under resource-constrained vehicular edge environments. The collaborative perception framework introduces a context-adaptive multi-modal fusion mechanism that dynamically weights sensor and model contributions, along with selective latent transmission and adaptive decoding for resource-aware operation. A lightweight VLM is integrated with an edge-assisted vehicular pipeline to support real-time on-vehicle inference with adaptive offloading based on latency and energy constraints, while a latent-space reasoning module enables cooperative decision-making. Experiments on BDD100K and nuScenes-QA show that LM-JEPA improves perception accuracy by 5% and reduces latency by approximately 7% over LLM and VLM baselines, while achieving up to 25% improvement in scene understanding, 20% higher intersection success rates, improved highway merging, and approximately 15% reduction in the transmitted model parameters. Full article

To address the difficulty of directly incorporating crop growth-stage information into industrial control processes and the limited adaptability of control parameters to different developmental stages in conventional greenhouse fertigation management, this study developed a vision-based growth-stage determination and PLC-based fertigation parameter invocation system for greenhouse blueberry cultivation. The system integrated greenhouse blueberry image acquisition, edge-based visual recognition, STM32-based encoding conversion, PLC control, human–machine interaction, and actuator linkage. Image samples were collected from greenhouse blueberry plants, whereas system-level linkage verification was conducted on a small greenhouse prototype platform. The edge vision module was used to output preliminary blueberry growth-stage labels, while environmental and substrate sensor data were used for sensor status verification and control safety validation. The final growth-stage label was converted by the STM32 unit into a discrete coded signal and then transmitted to the PLC. Based on a predefined stage-strategy table, the PLC invoked the corresponding target parameters and drove the irrigation, fertilizer delivery, supplemental lighting, ventilation, and shading devices for coordinated control. The image-level stage classification evaluation based on an independent test set showed that different lightweight YOLO classification models exhibited different performance levels in identifying the major growth stages of blueberry. YOLO11n-cls achieved the highest Accuracy and Macro F1-score, reaching 85.71% and 84.81%, respectively. YOLOv8n-cls achieved an Accuracy, Macro F1-score, and Macro AP of 80.95%, 81.10%, and 91.25%, respectively, showing a favorable balance between model size and recognition performance. The confusion matrix indicated that misclassifications mainly occurred between the fruit expansion stage and the ripening stage, reflecting the morphological continuity of blueberry fruit development during the transitional period. The system linkage test results showed that blueberry growth-stage labels could be output by the edge vision terminal, converted by the STM32 unit, read by the PLC, and used for stage-specific target parameter invocation. Sensor acquisition, HMI display, and actuator response were completed cooperatively. The single determination and output time of the edge terminal was 500–1000 ms, and the remote-control response delay was 0.3–1.0 s. No obvious communication interruption, command loss, or abnormal shutdown occurred during system operation. These results indicate that blueberry growth-stage recognition results can serve as input conditions for PLC parameter invocation and device-control testing on a small greenhouse prototype platform. This study did not conduct a complete closed-loop cultivation experiment under real production greenhouse conditions or establish long-term blueberry cultivation control treatments. Therefore, no quantitative conclusions are drawn regarding water and fertilizer use efficiency, fertilizer application reduction, plant physiological responses, yield, or fruit quality improvement. Full article

Accurate measurement of the 6-DOF motion responses of marine floating structures is essential for structural safety assessment and operational decision-making. To address the critical issues of integration drift in inertial navigation systems, susceptibility of GNSS to sea-surface multipath effects, and deployment complexity of binocular vision systems, this paper proposed a 6-DOF motion measurement method for floating structures based on monocular vision and natural feature point tracking. This method eliminates the reliance on artificial cooperative targets and auxiliary sensors, instead utilizing the inherent surface textures of the floating structures as feature sources. Stable feature point tracking is achieved through multi-strategy cascaded detection and the pyramidal KLT optical flow algorithm. RANSAC geometric consistency verification is introduced to eliminate outlier matches, retaining only identical physical points between two consecutive frames for motion estimation. In-plane translations and $RZ$ angle are extracted from the similarity transformation, while $RX$ and $RY$ angles are estimated using principal component analysis of the covariance matrix of the feature point set. The depth-direction displacement is linearly mapped from variations in the scale factor. Subsequently, two series of physical model tests under different conditions were conducted to validate the measurement accuracy and robustness of the proposed method on different floating structures. The results demonstrate that the proposed method can accurately capture the motion attitudes of floating structures, maintaining a consistently high inlier ratio exceeding 80% in regular waves and averaging 85.2% in irregular waves, with a reprojection error of less than 0.05 pixels. The NRMSE for the primary motion directions are all below 10%, and the dominant frequency errors are essentially zero. It offers advantages such as low cost, easy deployment, and strong robustness, thereby providing valuable technical support for field monitoring of marine floating structures. Full article

The path-tracking and motion control of autonomous tractor–trailer systems (TTSs) in unstructured off-road environments have become important research topics of intelligent equipment. Traditional control architectures face challenges due to multiple physical constraints such as time-varying soil rheology, time-varying wheel slip, multi-body nonlinear coupling, and vehicle stability constraints. This review provides an overview of advanced control technologies for autonomous TTSs in off-road environments. First, this review analyzes multi-source sensors and joint state estimation frameworks for time-varying terrain classification, all-wheel slip ratio estimation, and articulation angle. Secondly, nonholonomic kinematic, multi-body three-dimensional spatial dynamic, and tire–soil interaction mechanic models are deconstructed for on-axle and off-axle hitching configurations. On this basis, adaptive kinematic control, model predictive control (MPC), robust disturbance rejection, and distributed electric-drive control strategies are compared horizontally, and the application of active trailer steering, torque vectoring, and implemented chassis cooperative control in active safety is elaborated. Finally, emerging research directions, including online tire–soil interaction learning, embodied AI foundation models, and V2X-enabled multi-vehicle cooperative platooning, are discussed to provide insights into the development of next-generation fully autonomous off-road tractor–trailer systems. Full article

This paper presents the Micro Video Guidance Sensor Version 2 (uVGS-2), a ROS-based vision navigation framework for real-time six-degrees-of-freedom pose estimation in drones, spacecraft, and autonomous robotic platforms operating in GNSS-denied environments. The system evolves from the previous Smartphone Video Guidance Sensor (SVGS) architecture through a modular C++ implementation, including advanced image preprocessing, deterministic blob sorting, and an optimized perspective-4-point solver using a Lie-algebra-based analytical Jacobian formulation. The proposed architecture achieves computationally efficient photogrammetric state estimation using onboard camera and processor resources, enabling deployment in resource-constrained systems. Experimental validation was conducted in NASA’s Astrobee free-flying robot, both at the International Space Station (ISS), for SVGS, and by ground testing through real-time sensor-fusion with Astrobee’s graph-based localizer (Astroloc), for uVGS-2. Results demonstrate robust centimeter-level accuracy in relative position and attitude estimation under illumination disturbances, partial occlusions, and intermittent loss of line-of-sight. The framework can be used in robotic platforms and autonomous UAV operations, including precision landing, formation flight, and cooperative navigation in environments where GNSS signals are unavailable or intermittent. Full article

In order to address the issue of rapidly divergent positioning errors of a single-platform inertial navigation system (INS) in GNSS-denied environments, this paper proposes a cross-platform cooperative navigation method based on relative range and angle measurements. The observability of the cooperative navigation system under different motion strategies is investigated using Fisher information matrix (FIM) right null-space analysis combined with singular value decomposition (SVD). The results show that with relative range and angle measurement constraints, all inertial sensor biases can be effectively estimated by two strapdown inertial navigation systems (SINSs) moving along a simple trajectory, thereby improving the navigation accuracy. Experimental results demonstrate that compared to the autonomous navigation mode, the average positioning accuracy of the two SINSs improves by 77.4% and 68.4% respectively after 3 h of cooperative navigation along the prescribed trajectory. Using relative range and angle measurements, the proposed method requires only two SINSs and relatively simple planar motion, without the need for high-precision reference benchmarks, complex three-dimensional excitation trajectories, or turntable modulation. It reduces system complexity and motion requirements, providing an effective and easy-to-implement solution for ground vehicular positioning and orientation and other cross-platform cooperative navigation tasks in GNSS-denied environments. Full article

Flying Ad-Hoc Networks (FANETs), Internet of Things (IoT), and Internet of Vehicles (IoV) are critical enablers of intelligent transportation and smart city ecosystems. Their reliance on shared wireless channels, however, exposes them to diverse jamming attacks that threaten communication reliability, mission effectiveness, and safety. This paper presents a comprehensive study of jamming threats in integrated FANET–IoT–IoV environments and analyzes conventional and advanced anti-jamming techniques across physical, link/MAC, spectral, spatial, temporal, and hybrid domains. To address the challenges posed by heterogeneous and dynamic network conditions, we propose a cross-layer anti-jamming framework that integrates Cognitive Radio (CR) for dynamic spectrum access and Multi-Agent Reinforcement Learning (MARL) for cooperative, adaptive decision-making. The framework employs a Perception Engine for local anomaly detection, a Cognitive Engine for constructing a collaborative jamming map, and a Decision and Action Engine for multi-agent DRL-based mitigation. Simulation results demonstrate that the proposed CR-MARL framework significantly improves packet delivery ratio, reduces latency, and adapts efficiently to varying jamming strategies, while maintaining low energy and computational overhead, making it suitable for resource-constrained UAVs, vehicles, and IoT sensors. Full article

In-Mold Electronics (IMEs) present a highly promising monolithic integration strategy for manufacturing miniaturized 3D MEMS optical tweezers, offering exceptional environmental adaptability and structural compactness. However, the precision of such optical systems is heavily constrained by the injection molding process. During the molding phase, high-pressure melt scouring and severe thermo-mechanical coupling frequently induce geometric misalignment, manifesting as node displacement, localized warpage, and residual stress accumulation in the embedded circuits. This displacement critically alters the cross-sectional area of conductive traces, leading to resistance fluctuations that can destabilize the driving current. According to American Wire Gauge (AWG) standards, ensuring the geometric fidelity of this sensor-CPU interconnect pathway is fundamental to maintaining signal integrity. To address these manufacturing bottlenecks, this study systematically investigates the process stability of IME circuits Cyclic Olefin Copolymer (COC) is strategically selected as the substrate material over Polycarbonate (PC) and Liquid Silicone Rubber (LSR) due to its ultra-high light transmittance, extremely low water absorption, and superior thermomechanical stability. Based on finite element simulation, a data-driven intelligent optimization framework is developed. Latin Hypercube Sampling (LHS) is first utilized to efficiently sample the multi-dimensional process space, comprising melt temperature, packing pressure, and packing time. To handle the non-stationary nature of process feedback signals, wavelet analysis is introduced to decouple high-frequency noise, extracting Wavelet Energy Entropy (WEE) as a highly robust dynamic metric for process stability. Subsequently, a hybrid NSGA-II-MOPSO multi-objective algorithm is deployed to cooperatively optimize the injection parameters. The simulation-based optimization results demonstrate a substantial enhancement in manufacturing precision. Under the optimal parameter configuration, the average node displacement of the embedded circuits decreases significantly from 0.034 mm to 0.014 mm, achieving a 58.82% reduction. Simultaneously, volumetric shrinkage drops from 5.755% to 4.832% (a 16.04% reduction), while residual stress is maintained well within the structural safety threshold of optical-grade polymers. By clarifying the deformation control mechanism during the manufacturing phase, this study provides a highly reliable, data-driven methodological framework for the precision mass production of micro-nano optical systems. Full article