Fogs observed over Incheon international airport （IIA） in the west coast of Korea from January 2002 to August 2006 are classified into categories of coastal fog, cold sea fog, and warm sea fog based on the areal ext...Fogs observed over Incheon international airport （IIA） in the west coast of Korea from January 2002 to August 2006 are classified into categories of coastal fog, cold sea fog, and warm sea fog based on the areal extent of the fogs and the difference between the air temperature （T ） and the SST, i.e., cold sea fog if TSST = T -SST 〉 0~0C and warm sea fog if TSST 〈 0~0C. The numbers of coastal, cold, and warm sea fog cases are 64, 26, and 9. Coastal fogs form most frequently in winter, while cold sea fogs occur mostly in summer and warm sea fogs are observed from January to May but not in November and December. On average the air gets colder by 1.6~0C during the three hours leading up to the coastal fog formation, and an additional cooling of 1.1~0C occurs during the fog. The change in the dew point temperature （T_d） is minimal except during the fog （0.6~0C）. Decreases in T for the cold and warm sea fogs are relatively smaller. The average Td is higher than SST during the cold sea fog periods but this T_d is more than 4~0C higher than that for the corresponding non-fog days, suggesting that cold sea fogs be formed by the cooling of already humid air （i.e., T_d〉SST）. Increases of T_d are significant during the warm sea fog periods （1.4~0C）, implying that effcient moisture supply is essential to warm sea fog formation. Four major synoptic patterns are identified in association with the observed fogs. The most frequent is a north Pacific high that accounts for 38% of cases. Surface or upper inversions are present in 77%, 69%, and 81% of the fog periods for coastal, cold, and warm sea fogs, respectively.展开更多
A novel grey Markov chain predictive model is discussed to reduce drift influence on the output of fiber optical gyroscopes(FOGs)and to improve FOGs'measurement precision.The proposed method possesses advantages o...A novel grey Markov chain predictive model is discussed to reduce drift influence on the output of fiber optical gyroscopes(FOGs)and to improve FOGs'measurement precision.The proposed method possesses advantages of grey model and Markov chain.It makes good use of dynamic modeling idea of the grey model to predict general trend of original data.Then according to the trend,states are divided so that it can overcome the disadvantage of high computational cost of state transition probability matrix in Markov chain.Moreover,the presented approach expands the applied scope of the grey model and makes it be fit for prediction of random data with bigger fluctuation.The numerical results of real drift data from a certain type FOG verify the effectiveness of the proposed grey Markov chain model powerfully.The Markov chain is also investigated to provide a comparison with the grey Markov chain model.It is shown that the hybrid grey Markov chain prediction model has higher modeling precision than Markov chain itself,which prove this proposed method is very applicable and effective.展开更多
Fog simulation and prediction are becoming increasingly important in China because of the great impact of fog on traffic and other human activities. More studies are needed to have a better understanding of the format...Fog simulation and prediction are becoming increasingly important in China because of the great impact of fog on traffic and other human activities. More studies are needed to have a better understanding of the formation mechanisms and life cycles of fogs. This work uses data from two fog cases observed in Wuqing, Tianjin, in 2009. The data include aerosol size distribution, fog droplet size distribution, fog liquid water content, and meteorological properties. The results show that increasing aerosols can increase the number concentration of fog droplets and decrease fog droplet size, which is consistent with the first aerosol indirect effect found in clouds. It is also shown that increased aerosols can lead to lower visibility in fogs. This work demonstrates that the first aerosol indirect effect plays an important role in fogs.展开更多
In this paper, we evaluated comprehensively the structure and operation of open-loop interferometric optical fiber gyroscopes (IFOG). To complete the previous works, a digital approach to derive the rotation angle in ...In this paper, we evaluated comprehensively the structure and operation of open-loop interferometric optical fiber gyroscopes (IFOG). To complete the previous works, a digital approach to derive the rotation angle in optical fiber gyroscopes is investigated theoretically. Results are simulated by the MATLAB software;therefore we could compare the results in simulated area with the values derived from theory. Also, feedback Erbium-doped fiber amplifier (EFDA) FOGs, called FE-FOG, is categorized in closed-loop IFOGs. The procedure of finding the Sagnac shift for open-loop and closed-loop IFOG have been studied and compared to one another. The signal processing in the open-loop IFOG was simulated using Matlab software and for the closed-loop IFOG by PSCAD. In the open-loop IFOG the analogue formulation of the IFOG in order to extract the phase shift is analyzed. A novel and promising method for derivation of Sagnac phase shift based on digital finite impulse response filtering is proposed. Based on our simulation results, the reliability and accuracy of the method is determined. In the closed-loop IFOG, the shift was derived through frequent use of Sagnac loop. The output signal is injected in the input again as feedback. The shift phase between clockwise and counterclockwise waves in each complete route, including primary and feedback route, is identified as Sagnac shift phase.展开更多
Based on the principle of transient perturbation analysis,in this paper,a method to objectively determine the weather pattern formed by sea fog is provided.On the basis of the classification results,the circulation si...Based on the principle of transient perturbation analysis,in this paper,a method to objectively determine the weather pattern formed by sea fog is provided.On the basis of the classification results,the circulation situation,divergence and vertical velocity field,and the vertical profile of temperature and humidity are synthesized and analyzed.The basic characteristics of the circulation and physical field of sea fog under low pressure control(L type sea fog)are obtained,and the results are compared with the sea fog under the control of high pressure(H type sea fog):a)L type sea fogs potential height anomaly disturbance is mainly manifested in the low layer,and its average value is-65.66 gpm,gradually weakening upward;b)L type sea fogs inversion structure is weaker than H type sea fogs when it occurs,the fog layer is thicker and the high relative humidity level is high over the fog layer,while the H type sea fogs fog layer has a relatively obvious dry layer;c)L sea fog has three layers of structure at the vertical direction.The first layer 1000-950 hPa is convergence accompanied by weak rise and subsidence,the second layer 950-850 hPa is divergence accompanied by weak subsidence,and the third layer 850 to 500hPa is gradually strengthened.While there are two layer structures of the H type sea fog.1000 hPa is divergence accompanied by weak rising and sinking movement,950-500 hPa is a uniform subsidence movement.d)Probability density statistical analysis further quantified the vertical movement of L and H type sea fog and the distribution of relative humidity in each layer.These conclusions provide an important reference for forecasting the sea fog in the northwest of the Yellow Sea under the condition of low pressure circulation in summer.展开更多
Belonging to the southern subtropical moist type of monsoon climate, the Nanling mountainous area experiences heavy fogs whenever quasi-stationary fronts appear there from September to May. There can be as many as 15-...Belonging to the southern subtropical moist type of monsoon climate, the Nanling mountainous area experiences heavy fogs whenever quasi-stationary fronts appear there from September to May. There can be as many as 15-18 days of heavy fogs per month. Fogs have more serious consequences in the Lechang-Ruyuan section of the Beijing-Zhuhai Expressway （the longest expressway in China） that passes through the main part of the Nanling Mts., where the road rises from 200 m to more than 800 m above sea level （ASL）. For a major motorway in the mountainous areas of Nanling Mts., two multidisciplinary integrated field observations were carried out, which measured visibility by the naked eyes, visibility by instrument, spectrum of fogdrops, liquid water content （LWC） of fog, tethered sounding, dual-parameter low-level sounding, turbulence diffusion within fog layers, aerosol spectra of size and composition, sampled fog water compositions, and sampled rainwater compositions. Typical cases were probed for their analyses of synoptics, micro- and macro-structures and microphysics. It is understood that heavy fogs take place with high frequency in the area and bring about serious consequences. Being typical advection and upslope fogs, they are in essence low-lying clouds appearing at high altitudes, which are closely related with the activity of South China frontal processes, especially the South China quasi-stationary fronts, and reflect on the role of local terrain as well. The heavy fogs are characteristic of long duration, extremely low visibility, well-organized lumpshaped structure, large-size fog-drops, moderate concentration, high LWC, and stronger turbulent diffusion within the fog layers than in fine sky. They differ much from radiation fogs, which are better documented in previous study in China. It is found that fog LWC is in significant anti-correlation with visibility so that large LWC is associated with small visual range. It is also noted that one of the reasons for the fluctuation of characteristic quantities of micro-structure such as the LWC of fog in the area is, in addition to the inhomogeneous structure of the fog itself, the effect of advection and inhomogeneous underlying surface; during the translation of fog with the ambient wind, irregular upslope and cross-over movement is another reason for the inhomogeneous structure and fluctuation of fog. The spectrum of the aerosol size displays itself as the power function of monotonous descent. The concentration of submicrometer particles is even higher. The high-concentration sulfate particles found in the aerosols of Nanling Mts. are actually good nuclei for condensation, which are favorable for the formation of fog. The presence of fog can help cleanse the trace compositions in the atmosphere so that fog droplets contain high levels of polluting elements. In the meantime, compared to cloud droplets, fog droplets are easier to be captured by the vertical surfaces of objects on the land surface, such as vegetation and buildings to constitute another kind of cleansing process. In vast stretches of forest like the Nanling Mts., this kind of cleansing may be quite important. Studying the characteristic variation of fogs in the area realistically assists in setting up a forecast and warning system for local fogs and provides basic information for fog dispersal experiments.展开更多
Cloud services,favored by many enterprises due to their high flexibility and easy operation,are widely used for data storage and processing.However,the high latency,together with transmission overheads of the cloud ar...Cloud services,favored by many enterprises due to their high flexibility and easy operation,are widely used for data storage and processing.However,the high latency,together with transmission overheads of the cloud architecture,makes it difficult to quickly respond to the demands of IoT applications and local computation.To make up for these deficiencies in the cloud,fog computing has emerged as a critical role in the IoT applications.It decentralizes the computing power to various lower nodes close to data sources,so as to achieve the goal of low latency and distributed processing.With the data being frequently exchanged and shared between multiple nodes,it becomes a challenge to authorize data securely and efficiently while protecting user privacy.To address this challenge,proxy re-encryption(PRE)schemes provide a feasible way allowing an intermediary proxy node to re-encrypt ciphertext designated for different authorized data requesters without compromising any plaintext information.Since the proxy is viewed as a semi-trusted party,it should be taken to prevent malicious behaviors and reduce the risk of data leakage when implementing PRE schemes.This paper proposes a new fog-assisted identity-based PRE scheme supporting anonymous key generation,equality test,and user revocation to fulfill various IoT application requirements.Specifically,in a traditional identity-based public key architecture,the key escrow problem and the necessity of a secure channel are major security concerns.We utilize an anonymous key generation technique to solve these problems.The equality test functionality further enables a cloud server to inspect whether two candidate trapdoors contain an identical keyword.In particular,the proposed scheme realizes fine-grained user-level authorization while maintaining strong key confidentiality.To revoke an invalid user identity,we add a revocation list to the system flows to restrict access privileges without increasing additional computation cost.To ensure security,it is shown that our system meets the security notion of IND-PrID-CCA and OW-ID-CCA under the Decisional Bilinear Diffie-Hellman(DBDH)assumption.展开更多
The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.Thi...The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.This paper proposes FE-ACS(Fog-Edge Adaptive Cybersecurity System),a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge,fog,and cloud layers to optimize security efficacy,latency,and privacy.Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923,while maintaining significantly lower end-to-end latency(18.7 ms)compared to cloud-centric(152.3 ms)and fog-only(34.5 ms)architectures.The system exhibits exceptional scalability,supporting up to 38,000 devices with logarithmic performance degradation—a 67×improvement over conventional cloud-based approaches.By incorporating differential privacy mechanisms with balanced privacy-utility tradeoffs(ε=1.0–1.5),FE-ACS maintains 90%–93%detection accuracy while ensuring strong privacy guarantees for sensitive healthcare data.Computational efficiency analysis reveals that our architecture achieves a detection rate of 12,400 events per second with only 12.3 mJ energy consumption per inference.In healthcare risk assessment,FE-ACS demonstrates robust operational viability with low patient safety risk(14.7%)and high system reliability(94.0%).The proposed framework represents a significant advancement in distributed security architectures,offering a scalable,privacy-preserving,and real-time solution for protecting healthcare IoT ecosystems against evolving cyber threats.展开更多
Cloud data sharing is an important issue in modern times.To maintain the privacy and confidentiality of data stored in the cloud,encryption is an inevitable process before uploading the data.However,the centralized ma...Cloud data sharing is an important issue in modern times.To maintain the privacy and confidentiality of data stored in the cloud,encryption is an inevitable process before uploading the data.However,the centralized management and transmission latency of the cloud makes it difficult to support real-time processing and distributed access structures.As a result,fog computing and the Internet of Things(IoT)have emerged as crucial applications.Fog-assisted proxy re-encryption is a commonly adopted technique for sharing cloud ciphertexts.It allows a semitrusted proxy to transforma data owner’s ciphertext into another re-encrypted ciphertext intended for a data requester,without compromising any information about the original ciphertext.Yet,the user revocation and cloud ciphertext renewal problems still lack effective and secure mechanisms.Motivated by it,we propose a revocable conditional proxy re-encryption scheme offering ciphertext evolution(R-CPRE-CE).In particular,a periodically updated time key is used to revoke the user’s access privileges while an access condition prevents a malicious proxy from reencrypting unauthorized ciphertext.We also demonstrate that our scheme is provably secure under the notion of indistinguishability against adaptively chosen identity and chosen ciphertext attacks in the random oracle model.Performance analysis shows that our scheme reduces the computation time for a complete data access cycle from an initial query to the final decryption by approximately 47.05%compared to related schemes.展开更多
The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous c...The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods.展开更多
In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task schedul...In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.展开更多
In this paper, the microphysical relationships of 8 dense fog events collected from a comprehensive fog observation campaign carried out at Pancheng（32.2 N, 118.7 E） in the Nanjing area, China in the winter of 2007 ...In this paper, the microphysical relationships of 8 dense fog events collected from a comprehensive fog observation campaign carried out at Pancheng（32.2 N, 118.7 E） in the Nanjing area, China in the winter of 2007 are investigated. Positive correlations are found among key microphysical properties（cloud droplet number concentration, droplet size, spectral standard deviation, and liquid water content） in each case, suggesting that the dominant processes in these fog events are likely droplet nucleation with subsequent condensational growth and/or droplet deactivation via complete evaporation of some droplets. The abrupt broadening of the fog droplet spectra indicates the occurrence of the collision-coalescence processes as well, although not dominating. The combined efects of the dominant processes and collision-coalescence on microphysical relationships are further analyzed by dividing the dataset according to visibility or autoconversion threshold in each case. The result shows that the specific relationships of number concentration to volume-mean radius and spectral standard deviation depend on the competition between the compensation of small droplets due to nucleation-condensation and the loss of small droplets due to collision-coalescence. Generally, positive correlations are found for diferent visibility or autoconversion threshold ranges in most cases, although negative correlations sometimes appear with lower visibility or larger autoconversion threshold. Therefore, the compensation of small droplets is generally stronger than the loss, which is likely related to the sufcient fog condensation nuclei in this polluted area.展开更多
As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely...As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely used in aerospace, unmanned driving, and other fields. However, due to the temper-ature sensitivity of optical devices, the influence of environmen-tal temperature causes errors in FOG, thereby greatly limiting their output accuracy. This work researches on machine-learn-ing based temperature error compensation techniques for FOG. Specifically, it focuses on compensating for the bias errors gen-erated in the fiber ring due to the Shupe effect. This work pro-poses a composite model based on k-means clustering, sup-port vector regression, and particle swarm optimization algo-rithms. And it significantly reduced redundancy within the sam-ples by adopting the interval sequence sample. Moreover, met-rics such as root mean square error (RMSE), mean absolute error (MAE), bias stability, and Allan variance, are selected to evaluate the model’s performance and compensation effective-ness. This work effectively enhances the consistency between data and models across different temperature ranges and tem-perature gradients, improving the bias stability of the FOG from 0.022 °/h to 0.006 °/h. Compared to the existing methods utiliz-ing a single machine learning model, the proposed method increases the bias stability of the compensated FOG from 57.11% to 71.98%, and enhances the suppression of rate ramp noise coefficient from 2.29% to 14.83%. This work improves the accuracy of FOG after compensation, providing theoretical guid-ance and technical references for sensors error compensation work in other fields.展开更多
The 6G smart Fog Radio Access Network(F-RAN)is an integration of 6G network intelligence technologies and the F-RAN architecture.Its aim is to provide low-latency and high-performance services for massive access devic...The 6G smart Fog Radio Access Network(F-RAN)is an integration of 6G network intelligence technologies and the F-RAN architecture.Its aim is to provide low-latency and high-performance services for massive access devices.However,the performance of current 6G network intelligence technologies and its level of integration with the architecture,along with the system-level requirements for the number of access devices and limitations on energy consumption,have impeded further improvements in the 6G smart F-RAN.To better analyze the root causes of the network problems and promote the practical development of the network,this study used structured methods such as segmentation to conduct a review of the topic.The research results reveal that there are still many problems in the current 6G smart F-RAN.Future research directions and difficulties are also discussed.展开更多
Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and ...Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.展开更多
Federated learning combines with fog computing to transform data sharing into model sharing,which solves the issues of data isolation and privacy disclosure in fog computing.However,existing studies focus on centraliz...Federated learning combines with fog computing to transform data sharing into model sharing,which solves the issues of data isolation and privacy disclosure in fog computing.However,existing studies focus on centralized single-layer aggregation federated learning architecture,which lack the consideration of cross-domain and asynchronous robustness of federated learning,and rarely integrate verification mechanisms from the perspective of incentives.To address the above challenges,we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning(BSAFL)framework based on dual aggregation for cross-domain scenarios.In particular,we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between domains.Second,we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models'availability of intra-domain user.Furthermore,we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using blockchain.Finally,security analysis demonstrates the security and privacy effectiveness of BSAFL,and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.展开更多
The shortage of freshwater has become a global challenge,exacerbated by global warming and the rapid growth of the world’s population.Researchers across various fields have made numerous attempts to efficiently colle...The shortage of freshwater has become a global challenge,exacerbated by global warming and the rapid growth of the world’s population.Researchers across various fields have made numerous attempts to efficiently collect freshwater for human use.These efforts include seawater desalination through reverse osmosis or distillation,sewage treatment technologies,and atmospheric water harvesting.However,after thoroughly exploring traditional freshwater harvesting methods,it has become clear that bio-inspired fog harvesting technology offers new prospects due to its unique advantages of efficiency and sustainability.This paper systematically introduces the current principles of fog harvesting and wettability mechanism found in nature.It reviews the research status of combining bionic fog harvesting materials with textile science from two distinct dimensions.Additionally,it describes the practical applications of fog harvesting materials in agriculture,industry,and domestic water use,analyzes their prospects and feasibility in engineering projects,discusses potential challenges in practical applications,and envisions future trends and directions for the development of these materials.展开更多
基金supported by Grant No. R01-2008-000-12073-0 from the Basic Research Program of Korea Science & Engineering Foundation
文摘Fogs observed over Incheon international airport （IIA） in the west coast of Korea from January 2002 to August 2006 are classified into categories of coastal fog, cold sea fog, and warm sea fog based on the areal extent of the fogs and the difference between the air temperature （T ） and the SST, i.e., cold sea fog if TSST = T -SST 〉 0~0C and warm sea fog if TSST 〈 0~0C. The numbers of coastal, cold, and warm sea fog cases are 64, 26, and 9. Coastal fogs form most frequently in winter, while cold sea fogs occur mostly in summer and warm sea fogs are observed from January to May but not in November and December. On average the air gets colder by 1.6~0C during the three hours leading up to the coastal fog formation, and an additional cooling of 1.1~0C occurs during the fog. The change in the dew point temperature （T_d） is minimal except during the fog （0.6~0C）. Decreases in T for the cold and warm sea fogs are relatively smaller. The average Td is higher than SST during the cold sea fog periods but this T_d is more than 4~0C higher than that for the corresponding non-fog days, suggesting that cold sea fogs be formed by the cooling of already humid air （i.e., T_d〉SST）. Increases of T_d are significant during the warm sea fog periods （1.4~0C）, implying that effcient moisture supply is essential to warm sea fog formation. Four major synoptic patterns are identified in association with the observed fogs. The most frequent is a north Pacific high that accounts for 38% of cases. Surface or upper inversions are present in 77%, 69%, and 81% of the fog periods for coastal, cold, and warm sea fogs, respectively.
文摘A novel grey Markov chain predictive model is discussed to reduce drift influence on the output of fiber optical gyroscopes(FOGs)and to improve FOGs'measurement precision.The proposed method possesses advantages of grey model and Markov chain.It makes good use of dynamic modeling idea of the grey model to predict general trend of original data.Then according to the trend,states are divided so that it can overcome the disadvantage of high computational cost of state transition probability matrix in Markov chain.Moreover,the presented approach expands the applied scope of the grey model and makes it be fit for prediction of random data with bigger fluctuation.The numerical results of real drift data from a certain type FOG verify the effectiveness of the proposed grey Markov chain model powerfully.The Markov chain is also investigated to provide a comparison with the grey Markov chain model.It is shown that the hybrid grey Markov chain prediction model has higher modeling precision than Markov chain itself,which prove this proposed method is very applicable and effective.
基金supported by the Chinese National Public Benefit Research Foundation of Meteorology(Grants Nos. GYHY200906025 and GYHY201006011)
文摘Fog simulation and prediction are becoming increasingly important in China because of the great impact of fog on traffic and other human activities. More studies are needed to have a better understanding of the formation mechanisms and life cycles of fogs. This work uses data from two fog cases observed in Wuqing, Tianjin, in 2009. The data include aerosol size distribution, fog droplet size distribution, fog liquid water content, and meteorological properties. The results show that increasing aerosols can increase the number concentration of fog droplets and decrease fog droplet size, which is consistent with the first aerosol indirect effect found in clouds. It is also shown that increased aerosols can lead to lower visibility in fogs. This work demonstrates that the first aerosol indirect effect plays an important role in fogs.
文摘In this paper, we evaluated comprehensively the structure and operation of open-loop interferometric optical fiber gyroscopes (IFOG). To complete the previous works, a digital approach to derive the rotation angle in optical fiber gyroscopes is investigated theoretically. Results are simulated by the MATLAB software;therefore we could compare the results in simulated area with the values derived from theory. Also, feedback Erbium-doped fiber amplifier (EFDA) FOGs, called FE-FOG, is categorized in closed-loop IFOGs. The procedure of finding the Sagnac shift for open-loop and closed-loop IFOG have been studied and compared to one another. The signal processing in the open-loop IFOG was simulated using Matlab software and for the closed-loop IFOG by PSCAD. In the open-loop IFOG the analogue formulation of the IFOG in order to extract the phase shift is analyzed. A novel and promising method for derivation of Sagnac phase shift based on digital finite impulse response filtering is proposed. Based on our simulation results, the reliability and accuracy of the method is determined. In the closed-loop IFOG, the shift was derived through frequent use of Sagnac loop. The output signal is injected in the input again as feedback. The shift phase between clockwise and counterclockwise waves in each complete route, including primary and feedback route, is identified as Sagnac shift phase.
基金supported by National Natural Science Foundation of China(No.41576108 and No.41605006)Natural Science Foundation project of Shandong Province(No.ZR2016DB26).
文摘Based on the principle of transient perturbation analysis,in this paper,a method to objectively determine the weather pattern formed by sea fog is provided.On the basis of the classification results,the circulation situation,divergence and vertical velocity field,and the vertical profile of temperature and humidity are synthesized and analyzed.The basic characteristics of the circulation and physical field of sea fog under low pressure control(L type sea fog)are obtained,and the results are compared with the sea fog under the control of high pressure(H type sea fog):a)L type sea fogs potential height anomaly disturbance is mainly manifested in the low layer,and its average value is-65.66 gpm,gradually weakening upward;b)L type sea fogs inversion structure is weaker than H type sea fogs when it occurs,the fog layer is thicker and the high relative humidity level is high over the fog layer,while the H type sea fogs fog layer has a relatively obvious dry layer;c)L sea fog has three layers of structure at the vertical direction.The first layer 1000-950 hPa is convergence accompanied by weak rise and subsidence,the second layer 950-850 hPa is divergence accompanied by weak subsidence,and the third layer 850 to 500hPa is gradually strengthened.While there are two layer structures of the H type sea fog.1000 hPa is divergence accompanied by weak rising and sinking movement,950-500 hPa is a uniform subsidence movement.d)Probability density statistical analysis further quantified the vertical movement of L and H type sea fog and the distribution of relative humidity in each layer.These conclusions provide an important reference for forecasting the sea fog in the northwest of the Yellow Sea under the condition of low pressure circulation in summer.
基金the National Natural Science Foundation of China under Grant No.49975001.
文摘Belonging to the southern subtropical moist type of monsoon climate, the Nanling mountainous area experiences heavy fogs whenever quasi-stationary fronts appear there from September to May. There can be as many as 15-18 days of heavy fogs per month. Fogs have more serious consequences in the Lechang-Ruyuan section of the Beijing-Zhuhai Expressway （the longest expressway in China） that passes through the main part of the Nanling Mts., where the road rises from 200 m to more than 800 m above sea level （ASL）. For a major motorway in the mountainous areas of Nanling Mts., two multidisciplinary integrated field observations were carried out, which measured visibility by the naked eyes, visibility by instrument, spectrum of fogdrops, liquid water content （LWC） of fog, tethered sounding, dual-parameter low-level sounding, turbulence diffusion within fog layers, aerosol spectra of size and composition, sampled fog water compositions, and sampled rainwater compositions. Typical cases were probed for their analyses of synoptics, micro- and macro-structures and microphysics. It is understood that heavy fogs take place with high frequency in the area and bring about serious consequences. Being typical advection and upslope fogs, they are in essence low-lying clouds appearing at high altitudes, which are closely related with the activity of South China frontal processes, especially the South China quasi-stationary fronts, and reflect on the role of local terrain as well. The heavy fogs are characteristic of long duration, extremely low visibility, well-organized lumpshaped structure, large-size fog-drops, moderate concentration, high LWC, and stronger turbulent diffusion within the fog layers than in fine sky. They differ much from radiation fogs, which are better documented in previous study in China. It is found that fog LWC is in significant anti-correlation with visibility so that large LWC is associated with small visual range. It is also noted that one of the reasons for the fluctuation of characteristic quantities of micro-structure such as the LWC of fog in the area is, in addition to the inhomogeneous structure of the fog itself, the effect of advection and inhomogeneous underlying surface; during the translation of fog with the ambient wind, irregular upslope and cross-over movement is another reason for the inhomogeneous structure and fluctuation of fog. The spectrum of the aerosol size displays itself as the power function of monotonous descent. The concentration of submicrometer particles is even higher. The high-concentration sulfate particles found in the aerosols of Nanling Mts. are actually good nuclei for condensation, which are favorable for the formation of fog. The presence of fog can help cleanse the trace compositions in the atmosphere so that fog droplets contain high levels of polluting elements. In the meantime, compared to cloud droplets, fog droplets are easier to be captured by the vertical surfaces of objects on the land surface, such as vegetation and buildings to constitute another kind of cleansing process. In vast stretches of forest like the Nanling Mts., this kind of cleansing may be quite important. Studying the characteristic variation of fogs in the area realistically assists in setting up a forecast and warning system for local fogs and provides basic information for fog dispersal experiments.
基金supported in part by the National Science and Technology Council of Taiwan under the contract numbers NSTC 114-2221-E-019-055-MY2 and NSTC 114-2221-E-019-069.
文摘Cloud services,favored by many enterprises due to their high flexibility and easy operation,are widely used for data storage and processing.However,the high latency,together with transmission overheads of the cloud architecture,makes it difficult to quickly respond to the demands of IoT applications and local computation.To make up for these deficiencies in the cloud,fog computing has emerged as a critical role in the IoT applications.It decentralizes the computing power to various lower nodes close to data sources,so as to achieve the goal of low latency and distributed processing.With the data being frequently exchanged and shared between multiple nodes,it becomes a challenge to authorize data securely and efficiently while protecting user privacy.To address this challenge,proxy re-encryption(PRE)schemes provide a feasible way allowing an intermediary proxy node to re-encrypt ciphertext designated for different authorized data requesters without compromising any plaintext information.Since the proxy is viewed as a semi-trusted party,it should be taken to prevent malicious behaviors and reduce the risk of data leakage when implementing PRE schemes.This paper proposes a new fog-assisted identity-based PRE scheme supporting anonymous key generation,equality test,and user revocation to fulfill various IoT application requirements.Specifically,in a traditional identity-based public key architecture,the key escrow problem and the necessity of a secure channel are major security concerns.We utilize an anonymous key generation technique to solve these problems.The equality test functionality further enables a cloud server to inspect whether two candidate trapdoors contain an identical keyword.In particular,the proposed scheme realizes fine-grained user-level authorization while maintaining strong key confidentiality.To revoke an invalid user identity,we add a revocation list to the system flows to restrict access privileges without increasing additional computation cost.To ensure security,it is shown that our system meets the security notion of IND-PrID-CCA and OW-ID-CCA under the Decisional Bilinear Diffie-Hellman(DBDH)assumption.
基金supported by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01276).
文摘The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.This paper proposes FE-ACS(Fog-Edge Adaptive Cybersecurity System),a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge,fog,and cloud layers to optimize security efficacy,latency,and privacy.Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923,while maintaining significantly lower end-to-end latency(18.7 ms)compared to cloud-centric(152.3 ms)and fog-only(34.5 ms)architectures.The system exhibits exceptional scalability,supporting up to 38,000 devices with logarithmic performance degradation—a 67×improvement over conventional cloud-based approaches.By incorporating differential privacy mechanisms with balanced privacy-utility tradeoffs(ε=1.0–1.5),FE-ACS maintains 90%–93%detection accuracy while ensuring strong privacy guarantees for sensitive healthcare data.Computational efficiency analysis reveals that our architecture achieves a detection rate of 12,400 events per second with only 12.3 mJ energy consumption per inference.In healthcare risk assessment,FE-ACS demonstrates robust operational viability with low patient safety risk(14.7%)and high system reliability(94.0%).The proposed framework represents a significant advancement in distributed security architectures,offering a scalable,privacy-preserving,and real-time solution for protecting healthcare IoT ecosystems against evolving cyber threats.
基金supported in part by the National Science and Technology Council of Republic of China under the contract numbers NSTC 114-2221-E-019-055-MY2NSTC 114-2221-E-019-069.
文摘Cloud data sharing is an important issue in modern times.To maintain the privacy and confidentiality of data stored in the cloud,encryption is an inevitable process before uploading the data.However,the centralized management and transmission latency of the cloud makes it difficult to support real-time processing and distributed access structures.As a result,fog computing and the Internet of Things(IoT)have emerged as crucial applications.Fog-assisted proxy re-encryption is a commonly adopted technique for sharing cloud ciphertexts.It allows a semitrusted proxy to transforma data owner’s ciphertext into another re-encrypted ciphertext intended for a data requester,without compromising any information about the original ciphertext.Yet,the user revocation and cloud ciphertext renewal problems still lack effective and secure mechanisms.Motivated by it,we propose a revocable conditional proxy re-encryption scheme offering ciphertext evolution(R-CPRE-CE).In particular,a periodically updated time key is used to revoke the user’s access privileges while an access condition prevents a malicious proxy from reencrypting unauthorized ciphertext.We also demonstrate that our scheme is provably secure under the notion of indistinguishability against adaptively chosen identity and chosen ciphertext attacks in the random oracle model.Performance analysis shows that our scheme reduces the computation time for a complete data access cycle from an initial query to the final decryption by approximately 47.05%compared to related schemes.
基金appreciation to the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R384)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods.
基金supported and funded by theDeanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2503).
文摘In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.
基金Supported by National Natural Science Foundation of China (41305120,41030962,41275151,41375138,41375137,and 41305034)Natural Science Foundation of Jiangsu Province (BK20130988,SK201220841)+8 种基金Specialized Research Fund for the Doctoral Program of Higher Education (20133228120002)China Meteorological Administration Special Public Welfare Research Fund (GYHY201406007)Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (13KJB170014)Open Funding from Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration (KDW1201,KDW1102)Open Funding from Key Laboratory of Meteorological Disaster of Ministry of Education (KLME1205,KLME1107)Open Funding from State Key Laboratory of Severe Weather (2013LASW-B06)Qing-Lan Project for Cloud-Fog-Precipitation-Aerosol Study in Jiangsu ProvinceProject Funded by the Priority Academic Program Development of Jiangsu Higher Education InstitutionsU.S. Department of Energy’s (DOE) Earth System Modeling (ESM) program via the FASTER project (www.bnl.gov/faster) and Atmospheric System Research (ASR) program
文摘In this paper, the microphysical relationships of 8 dense fog events collected from a comprehensive fog observation campaign carried out at Pancheng（32.2 N, 118.7 E） in the Nanjing area, China in the winter of 2007 are investigated. Positive correlations are found among key microphysical properties（cloud droplet number concentration, droplet size, spectral standard deviation, and liquid water content） in each case, suggesting that the dominant processes in these fog events are likely droplet nucleation with subsequent condensational growth and/or droplet deactivation via complete evaporation of some droplets. The abrupt broadening of the fog droplet spectra indicates the occurrence of the collision-coalescence processes as well, although not dominating. The combined efects of the dominant processes and collision-coalescence on microphysical relationships are further analyzed by dividing the dataset according to visibility or autoconversion threshold in each case. The result shows that the specific relationships of number concentration to volume-mean radius and spectral standard deviation depend on the competition between the compensation of small droplets due to nucleation-condensation and the loss of small droplets due to collision-coalescence. Generally, positive correlations are found for diferent visibility or autoconversion threshold ranges in most cases, although negative correlations sometimes appear with lower visibility or larger autoconversion threshold. Therefore, the compensation of small droplets is generally stronger than the loss, which is likely related to the sufcient fog condensation nuclei in this polluted area.
基金supported by the National Natural Science Foundation of China(62375013).
文摘As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely used in aerospace, unmanned driving, and other fields. However, due to the temper-ature sensitivity of optical devices, the influence of environmen-tal temperature causes errors in FOG, thereby greatly limiting their output accuracy. This work researches on machine-learn-ing based temperature error compensation techniques for FOG. Specifically, it focuses on compensating for the bias errors gen-erated in the fiber ring due to the Shupe effect. This work pro-poses a composite model based on k-means clustering, sup-port vector regression, and particle swarm optimization algo-rithms. And it significantly reduced redundancy within the sam-ples by adopting the interval sequence sample. Moreover, met-rics such as root mean square error (RMSE), mean absolute error (MAE), bias stability, and Allan variance, are selected to evaluate the model’s performance and compensation effective-ness. This work effectively enhances the consistency between data and models across different temperature ranges and tem-perature gradients, improving the bias stability of the FOG from 0.022 °/h to 0.006 °/h. Compared to the existing methods utiliz-ing a single machine learning model, the proposed method increases the bias stability of the compensated FOG from 57.11% to 71.98%, and enhances the suppression of rate ramp noise coefficient from 2.29% to 14.83%. This work improves the accuracy of FOG after compensation, providing theoretical guid-ance and technical references for sensors error compensation work in other fields.
基金supported by the National Natural Science Foundation of China(62202215)Liaoning Province Applied Basic Research Program(Youth Special Project,2023JH2/101600038)+2 种基金Shenyang Youth Science and Technology Innovation Talent Support Program(RC220458)Guangxuan Program of Shenyang Ligong University(SYLUGXRC202216)Basic Research Special Funds for Undergraduate Universities in Liaoning Province(LJ212410144067).
文摘The 6G smart Fog Radio Access Network(F-RAN)is an integration of 6G network intelligence technologies and the F-RAN architecture.Its aim is to provide low-latency and high-performance services for massive access devices.However,the performance of current 6G network intelligence technologies and its level of integration with the architecture,along with the system-level requirements for the number of access devices and limitations on energy consumption,have impeded further improvements in the 6G smart F-RAN.To better analyze the root causes of the network problems and promote the practical development of the network,this study used structured methods such as segmentation to conduct a review of the topic.The research results reveal that there are still many problems in the current 6G smart F-RAN.Future research directions and difficulties are also discussed.
基金supported by the Deanship of Scientific Research and Graduate Studies at King Khalid University under research grant number(R.G.P.2/93/45).
文摘Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.
基金supported in part by the National Key Research and Development Program of China under Grant No.2021YFB3101100in part by the National Natural Science Foundation of China under Grant 62272123,62272102,62272124+2 种基金in part by the Project of High-level Innovative Talents of Guizhou Province under Grant[2020]6008in part by the Science and Technology Program of Guizhou Province under Grant No.[2020]5017,No.[2022]065in part by the Guangxi Key Laboratory of Cryptography and Information Security under Grant GCIS202105。
文摘Federated learning combines with fog computing to transform data sharing into model sharing,which solves the issues of data isolation and privacy disclosure in fog computing.However,existing studies focus on centralized single-layer aggregation federated learning architecture,which lack the consideration of cross-domain and asynchronous robustness of federated learning,and rarely integrate verification mechanisms from the perspective of incentives.To address the above challenges,we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning(BSAFL)framework based on dual aggregation for cross-domain scenarios.In particular,we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between domains.Second,we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models'availability of intra-domain user.Furthermore,we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using blockchain.Finally,security analysis demonstrates the security and privacy effectiveness of BSAFL,and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.
基金Shandong Provincial Key Research and Development Program(Major Scientific and Technological Innovation Project)(2021CXGC011001)Huafon Microfibre(Jiangsu)Co.Ltd.(2021120011000234)+1 种基金Textile Vision Basic Research Program(J202306)China Postdoctoral Science Foundation(No.2023M732103).
文摘The shortage of freshwater has become a global challenge,exacerbated by global warming and the rapid growth of the world’s population.Researchers across various fields have made numerous attempts to efficiently collect freshwater for human use.These efforts include seawater desalination through reverse osmosis or distillation,sewage treatment technologies,and atmospheric water harvesting.However,after thoroughly exploring traditional freshwater harvesting methods,it has become clear that bio-inspired fog harvesting technology offers new prospects due to its unique advantages of efficiency and sustainability.This paper systematically introduces the current principles of fog harvesting and wettability mechanism found in nature.It reviews the research status of combining bionic fog harvesting materials with textile science from two distinct dimensions.Additionally,it describes the practical applications of fog harvesting materials in agriculture,industry,and domestic water use,analyzes their prospects and feasibility in engineering projects,discusses potential challenges in practical applications,and envisions future trends and directions for the development of these materials.
