Search Results (274)

High accuracy and time synchronous aerosol optical depth (AOD) is essential for atmospheric correction (AC) of medium and high spatial resolution (MHSR) remote sensing data. However, existing high-resolution AOD retrieval methods often rely on sparsely distributed ground-based measurements, which limits their capacity to resolve fine-scale spatial heterogeneity and consequently constrains retrieval performance. To address this limitation, we propose a framework that takes GF-1 top-of-atmosphere (TOA) reflectance as input, where the model is first pre-trained using MCD19A2 as Pseudo-labels, with high-confidence samples weighted according to their spatial consistency and temporal stability, and then fine-tuned using Aerosol Robotic Network (AERONET) observations. This approach enables improved retrieval accuracy while better capturing surface variability. Validation across multiple regions demonstrates strong agreement with AOD measurements, achieving the correlation coefficient (R) of 0.941 and RMSE of 0.113. Compared to models without pretraining, the proportion of AOD retrievals within EE improves by 13%. While applied to AC, the corrected surface reflectance also shows strong consistency with in situ observations (R > 0.93, RMSE < 0.04). The proposed Trans-AODnet significantly enhances the accuracy and reliability of AOD inputs for AC of high-resolution wide-field sensors (e.g., GF-WFV), offering robust support for regional environmental monitoring and exhibiting strong potential for broader remote sensing applications. Full article

Space Surveillance and Tracking (SST) plays a crucial role in ensuring space safety. To this end, accurate and numerous observational resources are needed to build and maintain a catalog of space objects. In particular, it is essential to develop optimal observation strategies to maximize both the number and the quality of detections obtained from a sensor network. This represents a key step in the assessment of the network through simulations. This work presents the integrated development of sensor tasking strategies for optical systems and a track-to-track correlation pipeline within S$\mathrm{\Xi }$NSIT, a software environment designed to simulate sensor network configurations and evaluate cataloging performance. For high-altitude low Earth orbit (HLEO) targets, which are fast-moving and widely distributed, tasking strategies emphasize systematic scans of the Earth’s shadow boundary to exploit favorable phase angles and improve observational accuracy, while medium- and geostationary-Earth orbits (MEO–GEO) rely on equatorial-plane scans. The correlation pipeline employs Two-Body Integrals, uncertainty propagation, and a ${\chi }^2$-test with the Squared Mahalanobis Distance to associate tracks and perform initial orbit determination of newly detected objects. Results indicate that the integrated approach significantly enhances detection coverage, leading to greater catalog build-up efficiency and improved SST performance. Consequently, it facilitates the cataloging of numerous uncataloged objects within a reduced timeframe. Full article

Unmanned aerial vehicle (UAV) swarms offer an efficient solution for data collection from widely distributed ground users (GUs). However, incomplete environment information and frequent changes make it challenging for standard centralized planning or pure reinforcement learning approaches to simultaneously maintain global solution quality and local flexibility. We propose a hierarchical data collection framework for heterogeneous UAV-assisted wireless sensor networks (WSNs). A small set of high-capability UAVs (H-UAVs), equipped with substantial computational and communication resources, coordinate regional coverage, trajectory planning, and uplink transmission control for numerous resource-constrained low-capability UAVs (L-UAVs) across power-Voronoi-partitioned areas using multi-agent deep reinforcement learning (MADRL). Specifically, we employ Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to enhance H-UAVs’ decision-making capabilities and enable coordinated actions. The partitions are dynamically updated based on GUs’ data generation rates and L-UAV density to balance workload and adapt to environmental dynamics. Concurrently, a large number of L-UAVs with limited onboard resources perform self-organized data collection from GUs and execute opportunistic relaying to a remote access point (RAP) via H-UAVs. Within each Voronoi cell, L-UAV motion follows a weighted Vicsek model that incorporates GUs’ age of information (AoI), link quality, and congestion avoidance. This spatial decomposition combined with decentralized weak-swarm control enables scalability to large-scale L-UAV deployments. Experiments demonstrate that the proposed strong and weak agent MADDPG (SW-MADDPG) scheme reduces AoI by 30% and 21% compared to No-Voronoi and Heuristic-HUAV baselines, respectively. Full article

Road dust resuspension is widely recognized as a major contributor to traffic-related particulate matter (PM) in urban environments. Nevertheless, reported emission factors exhibit substantial variability. These discrepancies stem not only from the intrinsic complexity of the resuspension process but also from limitations in measurement techniques, which often fail to adequately control or characterize the influencing parameters. As a result, the contribution of each parameter remains difficult to isolate, leading to inconsistencies across studies. This study presents an experimental protocol developed to quantify PM10 and PM2.5 emission factors associated with vehicle-induced road dust resuspension. Experiments were conducted on a dedicated test track seeded with alumina particles of controlled mass and size distribution to simulate road dust. A network of microsensors was strategically deployed at multiple upwind and downwind locations to continuously monitor particle concentration variations during vehicle passages. Emission factors were derived through time integration of the mass flow rate of resuspended dust measured by the sensor network. The estimated PM10 emission factor showed excellent agreement, within 2.5%, with predictions from a literature-based formulation, thereby validating the accuracy and external relevance of the proposed protocol. In contrast, comparisons with U.S. EPA formulas and other empirical equations revealed substantially larger discrepancies, particularly for PM2.5, highlighting the persistent limitations of current modeling approaches. Full article

High-rise building areas (HRBs) play a crucial role in providing social and environmental services during the process of modern urbanization. Their large-scale, long-term spatial distribution characteristics have significant implications for fields such as urban planning and regional climate analysis. However, existing studies are largely limited to local regions and fixed-time-phase images. These studies are also influenced by differences in remote sensing image acquisition, such as regional architectural styles, lighting conditions, seasons, and sensor variations. This makes it challenging to achieve robust extraction across time and regions. To address these challenges, we propose an improved method for extracting HRBs that uses a Prior-Embedded Dual-Branch Neural Network (PEDNet). The dual-path design balances global features with local details. More importantly, we employ a window attention mechanism to introduce diverse prior information as embedded features. By integrating these features, our method becomes more robust against HRB image feature variations. We conducted extensive experiments using Sentinel-2 data from four typical cities. The results demonstrate that our method outperforms traditional models, such as FCN and U-Net, as well as more recent high-performance segmentation models, including DeepLabV3+ and BuildFormer. It effectively captures HRB features in remote sensing images, adapts to complex conditions, and provides a reliable tool for wide geographic span, cross-timestamp urban monitoring. It has practical applications for optimizing urban planning and improving the efficiency of resource management. Full article

Monitoring the health of power lines (PL) is essential for ensuring reliable power delivery, facilitating predictive maintenance, and maintaining a resilient grid infrastructure. Given the extensive length of PL networks, large numbers of wireless sensor nodes must be deployed, often in remote and harsh environments where battery replacement is costly and impractical. To address these limitations, this work proposes a hybrid energy-harvesting approach that combines piezoelectric and photovoltaic (PV) technologies to enable long-term, battery-free PL monitoring. The primary energy source is a compact, tunable, magnetically coupled piezoelectric vibrational energy harvester (VEH) that exploits local magnetic field distribution, inducing mechanical excitation of a cantilever and enabling the harvesting of vibrational energy near the PL at a frequency of 50 Hz. A complementary PV harvester is integrated to ensure operation during power outages or conditions where the piezoelectric excitation is reduced, thereby enhancing system robustness. Electromechanical characterization and a lumped-parameter model show good agreement with experimental results of the proposed VEH. The system is validated both on a PL test bench (5 A–10 A) and through inertial excitation using an electrodynamic shaker, demonstrating stable performance across a wide range of operating conditions. The combined hybrid architecture highlights a promising pathway toward self-sustaining, maintenance-free sensor nodes for next-generation power line monitoring. Finally, we demonstrate the feasibility of using such system for powering a WSN node by comparing the power produced by the proposed system with the power consumption of a potential application. Full article

Air pollution monitoring systems use distributed sensors that record dynamic environmental conditions, often producing large volumes of heterogeneous and stochastic data. Efficient aggregation of this data is essential for reducing communication overhead while maintaining the quality of information for decision making. In this paper, we propose an unsupervised learning approach for soft clustering of sensors in air pollution monitoring systems. Our method utilizes the Expectation–Maximization algorithm, which is an unsupervised machine learning method and probabilistic technique, to cluster sensors into distinct sets corresponding to normal and polluted zones. This clustering is driven by the need for a dynamic data transmission policy: sensors in polluted zones must intensify their operation for detailed monitoring, while sensors in clean zones can reduce reporting rates and transmit condensed data summaries to alleviate network load and conserve energy. The cluster membership probability enables a tunable trade-off between data redundancy and monitoring accuracy. The high efficiency of the proposed AI-based clustering is validated by the simulation results. Under common pollution scenarios and with adequate sample sizes, the EM algorithm exhibits a relative error below 5%. The presented approach provides a foundation for a wide range of intelligent and adaptive data aggregation protocols. Full article

Pleurotus ostreatus is valued for its nutritional, medicinal, economic, and ecological benefits and is widely used in the food, pharmaceutical, and environmental protection industries. Pleurotus ostreatus, as a highly perishable edible fungus, faces significant challenges in supply chain quality control and food safety due to its short shelf life. As consumer demand for food freshness and full traceability increases, there is an urgent need to establish a reliable traceability system that enables real-time monitoring, spoilage prevention, and quality assurance. This study focuses on the Pleurotus ostreatus supply chain and designs and implements a multi-role flexible traceability system that integrates blockchain and the Internet of Things. The system collects key production and storage environment parameters in real time through sensor networks and enhances data accuracy and robustness using an improved adaptive weighted fusion algorithm, enabling precise monitoring of the growth environment and quality risks. The system adopts a “link-chain” mapping mechanism for multi-chain storage and dynamic reorganization of business processes. It incorporates attribute-based encryption strategies and smart contracts to support tiered data access and secure sharing among multiple parties. Key information is stored on the blockchain to prevent tampering, while auxiliary data is stored in off-chain databases and the Interplanetary File System to ensure efficient and verifiable data queries. Deployed at Shandong Qihe Ecological Agriculture Co., Ltd., No. 517, Xilou Village, Kunlun Town, Zichuan District, 255000, Zibo City, Shandong Province, China, the system covers 12 cultivation units and 60 sensor nodes, recording over 50,000 traceable data points. Experimental results demonstrate that the system outperforms baseline methods in query latency, data consistency, and environmental monitoring accuracy. The improved fusion algorithm reduced the total variance of environmental data by 20%. In practical application, the system reduced the spoilage rate of Pleurotus ostreatus by approximately 12.3% and increased the quality inspection pass rate by approximately 15.4%, significantly enhancing the supply chain’s quality control and food safety capabilities. The results show that the framework is feasible and scalable in terms of information credibility and operational efficiency and significantly improves food quality and safety monitoring throughout the production, storage, and distribution of Pleurotus ostreatus. This study provides a viable technological path for spoilage prevention, quality tracking, and digital food safety supervision, offering valuable insights for both food science research and practical applications. Full article

Low-cost optical sensors for particulate matter (PM) monitoring, such as the SDS011, are widely used due to their affordability and ease of deployment. However, their accuracy strongly depends on aerosol properties and environmental conditions, necessitating reliable calibration. This study presents a theoretical and laboratory evaluation of a practical calibration method based on visibility sensors, which measure atmospheric light extinction and are readily available at many meteorological stations. Experiments were conducted in a controlled aerosol chamber, using SDS011 sensors, visibility sensors (FD70 and SWS250), and gravimetric samplers. The mass extinction coefficient was determined through parallel measurements of visibility and mass concentration, enabling conversion of optical signals into accurate PM values. The calibrated SDS011 sensors demonstrated consistent response with a stable normalization factor (dependent on aerosol type, wavelength, and particle size), allowing their deployment as a spatially distributed sensor network. Comparison with manufacturer calibration revealed substantial deviations due to differences in aerosol optical properties, highlighting the importance of application-specific calibration. The visibility-based approach enables real-time, continuous calibration of low-cost sensors with minimal equipment, offering a scalable solution for PM monitoring in resource-limited or remote environments. The method’s robustness under varying environmental conditions remains to be explored. Nevertheless, the results establish visibility-based calibration as a reliable and accessible framework for enhancing the accuracy of low-cost PM sensing technologies. The method enables scalable calibration with a single gravimetric reference and is suited for future field deployment in resource-limited settings, following additional validation under real atmospheric conditions. Full article

Multi-sensor fire detection technology has been widely adopted in practical applications; however, existing methods still suffer from high false alarm rates and inadequate adaptability in complex environments due to their limited capacity to capture deep time-series dependencies in sensor data. To enhance robustness and accuracy, this paper proposes a novel model named BiLSTM-LN-SA, which integrates a Bidirectional Long Short-Term Memory (BiLSTM) network with Layer Normalization (LN) and a Self-Attention (SA) mechanism. The BiLSTM module extracts intricate time-series features and long-term dependencies. The incorporation of Layer Normalization mitigates feature distribution shifts across different environments, thereby improving the model’s adaptability to cross-scenario data and its generalization capability. Simultaneously, the Self-Attention mechanism dynamically recalibrates the importance of features at different time steps, adaptively enhancing fire-critical information and enabling deeper, process-aware feature fusion. Extensive evaluation on a real-world dataset demonstrates the superiority of the BiLSTM-LN-SA model, which achieves a test accuracy of 98.38%, an F1-score of 0.98, and an AUC of 0.99, significantly outperforming existing methods including EIF-LSTM, rTPNN, and MLP. Notably, the model also maintains low false positive and false negative rates of 1.50% and 1.85%, respectively. Ablation studies further elucidate the complementary roles of each component: the self-attention mechanism is pivotal for dynamic feature weighting, while layer normalization is key to stabilizing the learning process. This validated design confirms the model’s strong generalization capability and practical reliability across varied environmental scenarios. Full article

In urban intelligent transportation systems, the real-time acquisition of network-wide traffic states is constrained by limited sensor density and high deployment costs. To address this challenge, this paper proposes a learnable Detection Point Selection Module (DPSM), which adaptively determines the most informative observation points through an end-to-end attention mechanism to support full-map traffic state estimation. Distinct from conventional fixed deployment strategies, DPSM provides an adaptive detector configuration that, under the same number of loop sensors, achieves significantly higher estimation accuracy by intelligently optimizing their placement. Specifically, the module takes normalized spatial and temporal information as input and generates an attention-based distribution to identify critical traffic flow readings, which are subsequently fed into various backbone prediction models, including fully connected networks, convolutional neural networks, and long short-term memory networks. Experiments on the real-world NGSIM-US101 dataset demonstrate that three variants—DPSM-NN, DPSM-CNN, and DPSM-LSTM—consistently outperform their corresponding baselines, with notable robustness under sparse observation scenarios. These results highlight the advantage of adaptive detector placement in maximizing the utility of limited sensors, effectively mitigating information loss from sparse deployments and offering a cost-efficient, scalable solution for urban traffic monitoring and control. Full article

Radon exposure is the second leading cause of lung cancer worldwide, yet monitoring strategies remain limited, expensive, and unevenly applied. Recent advances in the Internet of Things (IoT) offer the potential to change radon surveillance through low-cost, real-time, distributed sensing networks. This review consolidates emerging research on IoT-based radon monitoring, drawing from both primary radon studies and analogous applications in environmental IoT. A search across six major databases and relevant grey literature yielded only five radon-specific IoT studies, underscoring how new this research field is rather than reflecting a shortcoming of the review. To enhance the analysis, we delve into sensor physics, embedded system design, wireless protocols, and calibration techniques, incorporating lessons from established IoT sectors like indoor air quality, industrial safety, and volcanic gas monitoring. This interdisciplinary approach reveals that many technical and logistical challenges, such as calibration drift, power autonomy, connectivity, and scalability, have been addressed in related fields and can be adapted for radon monitoring. By uniting pioneering efforts within the broader context of IoT-enabled environmental sensing, this review provides a reference point and a future roadmap. It outlines key research priorities, including large-scale validation, standardized calibration methods, AI-driven analytics integration, and equitable deployment strategies. Although radon-focused IoT research is still at an early stage, current progress suggests it could make continuous exposure assessment more reliable, affordable, and widely accessible with clear public health benefits. Full article

Reliable and affordable data acquisition is crucial in industrial applications and critical infrastructure monitoring. However, common low-cost sensors with an I²C interface have limited range and low resistance to interference, which limits their deployment in demanding conditions. This study aimed to design and verify a universal module that bridges the I²C communication interface with the robust RS-485 industrial bus. A hardware module was designed and constructed to serve as a gateway. The core of the system is an STM32F0x1 microcontroller, which controls communication between the local I²C bus, designed to connect a wide range of sensors, and the RS-485 industrial interface. The design emphasizes robustness, including multi-level protection of power and communication circuits. The functionality of the proposed solution was verified by testing the prototype in real conditions. The module, equipped with a combined SHT30 temperature and humidity sensor, was deployed on the premises of the University of Žilina, Slovakia near transport infrastructure. The data collected from two weeks of continuous operation, recorded at ten-minute intervals, confirmed its reliable and error-free functionality. The result of this work is a modular and scalable platform that enables the easy integration of inexpensive sensors into robust industrial networks. This solution significantly reduces the cost and complexity of building distributed monitoring systems in areas such as transportation, industrial automation, and environmental monitoring. Full article

Motion blur is a complex phenomenon caused by the relative movement between an observed object and an imaging sensor during the exposure time, resulting in degradation in the image quality. Deep-learning-based methods, particularly convolutional neural networks (CNNs), have shown promise in motion deblurring. However, the small kernel sizes of CNNs limit their ability to achieve optimal performance. Moreover, supervised deep-learning-based deblurring methods often exhibit overfitting in their training datasets. Models trained on widely used synthetic blur datasets frequently fail to generalize in other blur domains in real-world scenarios and often produce undesired artifacts. To address these challenges, we propose the Spatial Feature Selection Network (SFSNet), which incorporates a Regional Feature Extractor (RFE) module to expand the receptive field and effectively select critical spatial features in order to improve the deblurring performance. In addition, we present the BlurMix dataset, which includes diverse blur types, as well as a meta-tuning strategy for effective blur domain adaptation. Our method enables the network to rapidly adapt to novel blur distributions with minimal additional training, and thereby improve generalization. The experimental results show that the meta-tuning variant of the SFSNet eliminates unwanted artifacts and significantly improves the deblurring performance across various blur domains. Full article

Wireless sensor networks (WSNs) are widely used in environmental monitoring, industrial automation, and smart cities. The Hybrid Energy-Efficient Distributed (HEED) protocol is a popular clustering algorithm designed to prolong network lifetime by balancing energy consumption among sensor nodes. However, HEED relies on simple heuristics for cluster-head (CH) selection, which may not fully exploit the complex spatiotemporal patterns in node energy and topology. This paper introduces a novel protocol, Deep Learning–Hybrid Energy-Efficient Distributed (DL-HEED), which, for the first time, integrates a Graph Neural Network (GNN) into the clustering process. By leveraging the relational structure of WSNs and a comprehensive set of node and network features—including residual energy, node degree, spatial position, and signal strength—DL-HEED enables intelligent, context-aware, and adaptive CH selection. DL-HEED leverages the relational structure of WSNs through deep learning, enabling more adaptive and energy-efficient cluster head selection than traditional heuristic-based protocols. Extensive simulations demonstrate that DL-HEED significantly outperforms classic HEED achieving up to 60% improvement in the network lifetime and energy efficiency as the network size increases. This work establishes DL-HEED as a robust, scalable, and practical solution for next-generation WSN deployments, marking a substantial advancement in the application of deep learning to resource-constrained IoT environments. Full article