Since there is lack of methodology to assess the performance of defogging algorithm and the existing assessment methods have some limitations,three new methods for assessing the defogging algorithm were proposed.One w...Since there is lack of methodology to assess the performance of defogging algorithm and the existing assessment methods have some limitations,three new methods for assessing the defogging algorithm were proposed.One was using synthetic foggy image simulated by image degradation model to assess the defogging algorithm in full-reference way.In this method,the absolute difference was computed between the synthetic image with and without fog.The other two were computing the fog density of gray level image or constructing assessment system of color image from human visual perception to assess the defogging algorithm in no-reference way.For these methods,an assessment function was defined to evaluate algorithm performance from the function value.Using the defogging algorithm comparison,the experimental results demonstrate the effectiveness and reliability of the proposed methods.展开更多
Large-capacity compressors in industrial plants and the compressors in gas turbine engines consume a considerable amount of power. The compression work is a strong fimction of the ambient air temperature. This increas...Large-capacity compressors in industrial plants and the compressors in gas turbine engines consume a considerable amount of power. The compression work is a strong fimction of the ambient air temperature. This increase in compression work presents a significant problem to utilities, generators and power producers when electric demands are high during the hot months. In many petrochemical process industries and gas turbine engines, the in- crease in compression work curtails plant output, demanding more electric power to drive the system. One way to counter this problem is to directly cool the inlet air. Inlet fogging is a popular means of cooling the inlet air to air compressors. In the present study, experiments have been performed to investigate the suitability of two-fluid nozzle for inlet fogging. Compressed air is used as the driving working gas for two-fluid nozzle and water at am- bient conditions is dragged into the high-speed air jet, thus enabling the entrained water to be atomized in a very short distance from the exit of the two-fluid nozzle. The air supply pressure is varied between 2.0 and 5.0 bar and the water flow rate entrained is measured. The flow visualization and temperature and relative humidity measurements are carried out to specify the fogging characteristics of the two-fluid nozzle.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
In order to solve the drifting away of thermal fog droplets during thermal spraying and the incompatibility between fog droplet carrier and conventional com- mercial agro-chemicals, the fog droplet carrier, surfactant...In order to solve the drifting away of thermal fog droplets during thermal spraying and the incompatibility between fog droplet carrier and conventional com- mercial agro-chemicals, the fog droplet carrier, surfactant, condensation nucleus ma- terial and antifreeze, dispersant, thickener and defoamer were screened and assem- bled to develop a thermal fog sedimentation stabilizer in this study, thereby provid- ing technical support for application and promotion of thermal spraying technology in pest and disease control in crops.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
Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling...Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling.Since this is an NP-hard problem type,a metaheuristic approach can be a good option.This study introduces a novel enhancement to the Artificial Rabbits Optimization(ARO)algorithm by integrating Chaotic maps and Levy flight strategies(CLARO).This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence speed.It is designed for task scheduling in fog-cloud environments,optimizing energy consumption,makespan,and execution time simultaneously three critical parameters often treated individually in prior works.Unlike conventional single-objective methods,the proposed approach incorporates a multi-objective fitness function that dynamically adjusts the weight of each parameter,resulting in better resource allocation and load balancing.In analysis,a real-world dataset,the Open-source Google Cloud Jobs Dataset(GoCJ_Dataset),is used for performance measurement,and analyses are performed on three considered parameters.Comparisons are applied with well-known algorithms:GWO,SCSO,PSO,WOA,and ARO to indicate the reliability of the proposed method.In this regard,performance evaluation is performed by assigning these tasks to Virtual Machines(VMs)in the resource pool.Simulations are performed on 90 base cases and 30 scenarios for each evaluation parameter.The results indicated that the proposed algorithm achieved the best makespan performance in 80% of cases,ranked first in execution time in 61%of cases,and performed best in the final parameter in 69% of cases.In addition,according to the obtained results based on the defined fitness function,the proposed method(CLARO)is 2.52%better than ARO,3.95%better than SCSO,5.06%better than GWO,8.15%better than PSO,and 9.41%better than WOA.展开更多
Fog is a highly complex weather phenomenon influenced by numerous factors.This study investigated the impact of the Changbai Mountains’topography on the formation and development of spring fog in the Bohai Sea.From 1...Fog is a highly complex weather phenomenon influenced by numerous factors.This study investigated the impact of the Changbai Mountains’topography on the formation and development of spring fog in the Bohai Sea.From 12 to 14 May 2021,the Bohai region experienced a sea fog event.Utilizing Himawari-8 satellite data,ERA5 reanalysis dataset,land and sea station observations,the WRF model,a topography sensitivity experiment,and backward trajectory tracking,the influence of the Changbai Mountains’topography on the evolution of this sea fog event was assessed.Results indicated that the Changbai Mountains’topography significantly impacted the propagation and concentration of the sea fog through dual effects—namely,the Venturi Effect and Foehn Clearance Effect.Comparative simulations incorporating and excluding the Changbai Mountains revealed that its topography favored weak convergence(Venturi Effect)of low-level airflow over the Bohai Sea induced by a high-pressure system,promoting westward fog expansion.Additionally,the backward trajectory analysis further indicated that the Foehn Clearance Effect of the Changbai Mountains extended its influence far beyond the immediate lee side,contributing to significant changes in atmospheric conditions such as reductions in relative humidity and increases in potential temperature.The dry,warm foehn contributed to a reduction in the liquid water content,ultimately leading to the weakening or even dissipation of the sea fog in the region close to the Changbai Mountains.This study emphasizes the crucial role of the Changbai Mountains’topography in the development and evolution of fog,providing valuable insights for forecasting fog in regions with complex terrain.展开更多
Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple parties.When combined with Fog Computing,FL offers enhanced capabilities for machin...Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple parties.When combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data aggregation.To address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing scenarios.Specifically,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node dropping.Then,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication overhead.To minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive score.Theoretical analysis demonstrates that EPRFL offers robust security and low latency.Extensive experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.展开更多
The Internet of Things(IoT)has emerged as an important future technology.IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data.In IoT-Fog computing,resource allocation ...The Internet of Things(IoT)has emerged as an important future technology.IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data.In IoT-Fog computing,resource allocation and independent task scheduling aim to deliver short response time services demanded by the IoT devices and performed by fog servers.The heterogeneity of the IoT-Fog resources and the huge amount of data that needs to be processed by the IoT-Fog tasks make scheduling fog computing tasks a challenging problem.This study proposes an Adaptive Firefly Algorithm(AFA)for dependent task scheduling in IoT-Fog computing.The proposed AFA is a modified version of the standard Firefly Algorithm(FA),considering the execution times of the submitted tasks,the impact of synchronization requirements,and the communication time between dependent tasks.As IoT-Fog computing depends mainly on distributed fog node servers that receive tasks in a dynamic manner,tackling the communications and synchronization issues between dependent tasks is becoming a challenging problem.The proposed AFA aims to address the dynamic nature of IoT-Fog computing environments.The proposed AFA mechanism considers a dynamic light absorption coefficient to control the decrease in attractiveness over iterations.The proposed AFA mechanism performance was benchmarked against the standard Firefly Algorithm(FA),Puma Optimizer(PO),Genetic Algorithm(GA),and Ant Colony Optimization(ACO)through simulations under light,typical,and heavy workload scenarios.In heavy workloads,the proposed AFA mechanism obtained the shortest average execution time,968.98 ms compared to 970.96,1352.87,1247.28,and 1773.62 of FA,PO,GA,and ACO,respectively.The simulation results demonstrate the proposed AFA’s ability to rapidly converge to optimal solutions,emphasizing its adaptability and efficiency in typical and heavy workloads.展开更多
Federated Learning(FL)has become a popular training paradigm in recent years.However,stragglers are critical bottlenecks in an Internet of Things(IoT)network while training.These nodes produce stale updates to the ser...Federated Learning(FL)has become a popular training paradigm in recent years.However,stragglers are critical bottlenecks in an Internet of Things(IoT)network while training.These nodes produce stale updates to the server,which slow down the convergence.In this paper,we studied the impact of the stale updates on the global model,which is observed to be significant.To address this,we propose a weighted averaging scheme,FedStrag,that optimizes the training with stale updates.The work is focused on training a model in an IoT network that has multiple challenges,such as resource constraints,stragglers,network issues,device heterogeneity,etc.To this end,we developed a time-bounded asynchronous FL paradigm that can train a model on the continuous iflow of data in the edge-fog-cloud continuum.To test the FedStrag approach,a model is trained with multiple stragglers scenarios on both Independent and Identically Distributed(IID)and non-IID datasets on Raspberry Pis.The experiment results suggest that the FedStrag outperforms the baseline FedAvg in all possible cases.展开更多
Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G applications.Artificial Intelligence(AI)algorithms will ...Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G applications.Artificial Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and reliability.In this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task completion.However,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource wastage.Additionally,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities problem.This paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH scenarios.Additionally,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH scenarios.The performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed approach.The simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.展开更多
With the rapid advancement of ICT and IoT technologies,the integration of Edge and Fog Computing has become essential to meet the increasing demands for real-time data processing and network efficiency.However,these t...With the rapid advancement of ICT and IoT technologies,the integration of Edge and Fog Computing has become essential to meet the increasing demands for real-time data processing and network efficiency.However,these technologies face critical security challenges,exacerbated by the emergence of quantum computing,which threatens traditional encryption methods.The rise in cyber-attacks targeting IoT and Edge/Fog networks underscores the need for robust,quantum-resistant security solutions.To address these challenges,researchers are focusing on Quantum Key Distribution and Post-Quantum Cryptography,which utilize quantum-resistant algorithms and the principles of quantum mechanics to ensure data confidentiality and integrity.This paper reviews the current security practices in IoT and Edge/Fog environments,explores the latest advancements in QKD and PQC technologies,and discusses their integration into distributed computing systems.Additionally,this paper proposes an enhanced QKD protocol combining the Cascade protocol and Kyber algorithm to address existing limitations.Finally,we highlight future research directions aimed at improving the scalability,efficiency,and practicality of QKD and PQC for securing IoT and Edge/Fog networks against evolving quantum threats.展开更多
Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resourc...Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resource bottlenecks and increased energy consumption.This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management(QIARM)model,which introduces novel algorithms inspired by quantum principles for enhanced resource allocation.QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically.In addition,an energy-aware scheduling module minimizes power consumption by selecting optimal configurations based on energy metrics.The simulation was carried out in a 360-minute environment with eight distinct scenarios.This study introduces a novel quantum-inspired resource management framework that achieves up to 98%task offload success and reduces energy consumption by 20%,addressing critical challenges of scalability and efficiency in dynamic fog computing environments.展开更多
Vehicular networks enable seamless connectivity for exchanging emergency and infotainment content.However,retrieving infotainment data from remote servers often introduces high delays,degrading the Quality of Service(...Vehicular networks enable seamless connectivity for exchanging emergency and infotainment content.However,retrieving infotainment data from remote servers often introduces high delays,degrading the Quality of Service(QoS).To overcome this,caching frequently requested content at fog-enabled Road Side Units(RSUs)reduces communication latency.Yet,the limited caching capacity of RSUs makes it impractical to store all contents with varying sizes and popularity.This research proposes an efficient content caching algorithm that adapts to dynamic vehicular demands on highways to maximize request satisfaction.The scheme is evaluated against Intelligent Content Caching(ICC)and Random Caching(RC).The obtained results show that our proposed scheme entertains more contentrequesting vehicles as compared to ICC and RC,with 33%and 41%more downloaded data in 28%and 35%less amount of time from ICC and RC schemes,respectively.展开更多
Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is con...Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is considered,where multiple User Equipments(UEs)offload their computational tasks to the F-RAN through fog nodes.Each UE can select one of the fog nodes to offload its task,and each fog node may serve multiple UEs.The tasks are computed by the fog nodes or further offloaded to the cloud via a capacity-limited fronhaul link.In order to compute all UEs'tasks quickly,joint optimization of UE-Fog association,radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs.This min-max problem is formulated as a Mixed Integer Nonlinear Program(MINP).To tackle it,first,MINP is reformulated as a continuous optimization problem,and then the Majorization Minimization(MM)method is used to find a solution.The MM approach that we develop is unconventional in that each MM subproblem is solved inexactly with the same provable convergence guarantee as the exact MM,thereby reducing the complexity of MM iteration.In addition,a cooperative offloading model is considered,where the fog nodes compress-and-forward their received signals to the cloud.Under this model,a similar min-max latency optimization problem is formulated and tackled by the inexact MM.Simulation results show that the proposed algorithms outperform some offloading strategies,and that the cooperative offloading can exploit transmission diversity better than noncooperative offloading to achieve better latency performance.展开更多
In the rapidly evolving landscape of Industry 4.0(I4.0),the convergence of information and operational technologies necessitates real-time communication and collaboration across cyber-physical systems and the Internet...In the rapidly evolving landscape of Industry 4.0(I4.0),the convergence of information and operational technologies necessitates real-time communication and collaboration across cyber-physical systems and the Internet of Things(IoT).Rapid data transmission is particularly critical within enterprises(vertically)and among stakeholders(horizontally)in this complex,heterogeneous ecosystem.While current research has focused on data application,processing,and storage within the cloud-edge-device continuum,cross-edge transmission has received less attention,resulting in challenges such as suboptimal routing and excessive delays in horizontal communications.To address the above issues,this paper introduces a Connection-As-Required Scheme(CARS)specifically designed for delay-sensitive IoT and Cyber-Physical System(CPS)applications,where low-latency communication is essential for operational efficiency.CARS leverages Lyapunov optimization and backpressure algorithms to optimize traffic scheduling and routing,minimizing communication delay between entities.Benchmarking against state-of-the-art solutions,CARS reduces Round-Trip Time(RTT)to approximately 47.0%of conventional methods and decreases delay by 24.5%in TCP-based and 26.0%in UDP-based applications.These results highlight the potential of CARS to facilitate effective,low-latency collaboration in diverse I4.0 environments.展开更多
基金Projects(91220301,61175064,61273314)supported by the National Natural Science Foundation of ChinaProject(126648)supported by the Postdoctoral Science Foundation of Central South University,ChinaProject(2012170301)supported by the New Teacher Fund for School of Information Science and Engineering,Central South University,China
文摘Since there is lack of methodology to assess the performance of defogging algorithm and the existing assessment methods have some limitations,three new methods for assessing the defogging algorithm were proposed.One was using synthetic foggy image simulated by image degradation model to assess the defogging algorithm in full-reference way.In this method,the absolute difference was computed between the synthetic image with and without fog.The other two were computing the fog density of gray level image or constructing assessment system of color image from human visual perception to assess the defogging algorithm in no-reference way.For these methods,an assessment function was defined to evaluate algorithm performance from the function value.Using the defogging algorithm comparison,the experimental results demonstrate the effectiveness and reliability of the proposed methods.
基金supported by the Ministry of Education,Science Technology (MEST) and Korea Institute for Advancement of Technology (KIAT) through the Human Resource Training Project for Regional Innovation
文摘Large-capacity compressors in industrial plants and the compressors in gas turbine engines consume a considerable amount of power. The compression work is a strong fimction of the ambient air temperature. This increase in compression work presents a significant problem to utilities, generators and power producers when electric demands are high during the hot months. In many petrochemical process industries and gas turbine engines, the in- crease in compression work curtails plant output, demanding more electric power to drive the system. One way to counter this problem is to directly cool the inlet air. Inlet fogging is a popular means of cooling the inlet air to air compressors. In the present study, experiments have been performed to investigate the suitability of two-fluid nozzle for inlet fogging. Compressed air is used as the driving working gas for two-fluid nozzle and water at am- bient conditions is dragged into the high-speed air jet, thus enabling the entrained water to be atomized in a very short distance from the exit of the two-fluid nozzle. The air supply pressure is varied between 2.0 and 5.0 bar and the water flow rate entrained is measured. The flow visualization and temperature and relative humidity measurements are carried out to specify the fogging characteristics of the two-fluid nozzle.
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 by Anhui Agricultural Science and Technology Innovation Fund(16A1132)Science and Technology Major Project of Anhui Province(15CZZ03132)Special Fund for Talent Development in Anhui Province(13C1109)~~
文摘In order to solve the drifting away of thermal fog droplets during thermal spraying and the incompatibility between fog droplet carrier and conventional com- mercial agro-chemicals, the fog droplet carrier, surfactant, condensation nucleus ma- terial and antifreeze, dispersant, thickener and defoamer were screened and assem- bled to develop a thermal fog sedimentation stabilizer in this study, thereby provid- ing technical support for application and promotion of thermal spraying technology in pest and disease control in crops.
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.
基金the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number(R-2025-1567).
文摘Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling.Since this is an NP-hard problem type,a metaheuristic approach can be a good option.This study introduces a novel enhancement to the Artificial Rabbits Optimization(ARO)algorithm by integrating Chaotic maps and Levy flight strategies(CLARO).This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence speed.It is designed for task scheduling in fog-cloud environments,optimizing energy consumption,makespan,and execution time simultaneously three critical parameters often treated individually in prior works.Unlike conventional single-objective methods,the proposed approach incorporates a multi-objective fitness function that dynamically adjusts the weight of each parameter,resulting in better resource allocation and load balancing.In analysis,a real-world dataset,the Open-source Google Cloud Jobs Dataset(GoCJ_Dataset),is used for performance measurement,and analyses are performed on three considered parameters.Comparisons are applied with well-known algorithms:GWO,SCSO,PSO,WOA,and ARO to indicate the reliability of the proposed method.In this regard,performance evaluation is performed by assigning these tasks to Virtual Machines(VMs)in the resource pool.Simulations are performed on 90 base cases and 30 scenarios for each evaluation parameter.The results indicated that the proposed algorithm achieved the best makespan performance in 80% of cases,ranked first in execution time in 61%of cases,and performed best in the final parameter in 69% of cases.In addition,according to the obtained results based on the defined fitness function,the proposed method(CLARO)is 2.52%better than ARO,3.95%better than SCSO,5.06%better than GWO,8.15%better than PSO,and 9.41%better than WOA.
基金jointly supported by the National Natural Science Foundation of China[grant number 42205009]the Open Grants of the State Key Laboratory of Severe Weather[grant number 2024LASWB23]+1 种基金the Collaborative Innovation Project for Marine Meteorological Science and Technology in the Bohai Rim Region[grant number QYXM202315]the Research and Development Project of Hebei Provincial Meteorological Bureau[grant number 22ky26]。
文摘Fog is a highly complex weather phenomenon influenced by numerous factors.This study investigated the impact of the Changbai Mountains’topography on the formation and development of spring fog in the Bohai Sea.From 12 to 14 May 2021,the Bohai region experienced a sea fog event.Utilizing Himawari-8 satellite data,ERA5 reanalysis dataset,land and sea station observations,the WRF model,a topography sensitivity experiment,and backward trajectory tracking,the influence of the Changbai Mountains’topography on the evolution of this sea fog event was assessed.Results indicated that the Changbai Mountains’topography significantly impacted the propagation and concentration of the sea fog through dual effects—namely,the Venturi Effect and Foehn Clearance Effect.Comparative simulations incorporating and excluding the Changbai Mountains revealed that its topography favored weak convergence(Venturi Effect)of low-level airflow over the Bohai Sea induced by a high-pressure system,promoting westward fog expansion.Additionally,the backward trajectory analysis further indicated that the Foehn Clearance Effect of the Changbai Mountains extended its influence far beyond the immediate lee side,contributing to significant changes in atmospheric conditions such as reductions in relative humidity and increases in potential temperature.The dry,warm foehn contributed to a reduction in the liquid water content,ultimately leading to the weakening or even dissipation of the sea fog in the region close to the Changbai Mountains.This study emphasizes the crucial role of the Changbai Mountains’topography in the development and evolution of fog,providing valuable insights for forecasting fog in regions with complex terrain.
基金supported in part by the National Natural Science Foundation of China(62462053)the Science and Technology Foundation of Qinghai Province(2023-ZJ-731)+1 种基金the Open Project of the Qinghai Provincial Key Laboratory of Restoration Ecology in Cold Area(2023-KF-12)the Open Research Fund of Guangdong Key Laboratory of Blockchain Security,Guangzhou University。
文摘Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple parties.When combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data aggregation.To address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing scenarios.Specifically,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node dropping.Then,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication overhead.To minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive score.Theoretical analysis demonstrates that EPRFL offers robust security and low latency.Extensive experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.
基金the Deanship of Graduate Studies and Scientific Research at Najran University for funding this work under the Easy Funding Program grant code(NU/EFP/SERC/13/166).
文摘The Internet of Things(IoT)has emerged as an important future technology.IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data.In IoT-Fog computing,resource allocation and independent task scheduling aim to deliver short response time services demanded by the IoT devices and performed by fog servers.The heterogeneity of the IoT-Fog resources and the huge amount of data that needs to be processed by the IoT-Fog tasks make scheduling fog computing tasks a challenging problem.This study proposes an Adaptive Firefly Algorithm(AFA)for dependent task scheduling in IoT-Fog computing.The proposed AFA is a modified version of the standard Firefly Algorithm(FA),considering the execution times of the submitted tasks,the impact of synchronization requirements,and the communication time between dependent tasks.As IoT-Fog computing depends mainly on distributed fog node servers that receive tasks in a dynamic manner,tackling the communications and synchronization issues between dependent tasks is becoming a challenging problem.The proposed AFA aims to address the dynamic nature of IoT-Fog computing environments.The proposed AFA mechanism considers a dynamic light absorption coefficient to control the decrease in attractiveness over iterations.The proposed AFA mechanism performance was benchmarked against the standard Firefly Algorithm(FA),Puma Optimizer(PO),Genetic Algorithm(GA),and Ant Colony Optimization(ACO)through simulations under light,typical,and heavy workload scenarios.In heavy workloads,the proposed AFA mechanism obtained the shortest average execution time,968.98 ms compared to 970.96,1352.87,1247.28,and 1773.62 of FA,PO,GA,and ACO,respectively.The simulation results demonstrate the proposed AFA’s ability to rapidly converge to optimal solutions,emphasizing its adaptability and efficiency in typical and heavy workloads.
基金supported by SERB,India,through grant CRG/2021/003888financial support to UoH-IoE by MHRD,India(F11/9/2019-U3(A)).
文摘Federated Learning(FL)has become a popular training paradigm in recent years.However,stragglers are critical bottlenecks in an Internet of Things(IoT)network while training.These nodes produce stale updates to the server,which slow down the convergence.In this paper,we studied the impact of the stale updates on the global model,which is observed to be significant.To address this,we propose a weighted averaging scheme,FedStrag,that optimizes the training with stale updates.The work is focused on training a model in an IoT network that has multiple challenges,such as resource constraints,stragglers,network issues,device heterogeneity,etc.To this end,we developed a time-bounded asynchronous FL paradigm that can train a model on the continuous iflow of data in the edge-fog-cloud continuum.To test the FedStrag approach,a model is trained with multiple stragglers scenarios on both Independent and Identically Distributed(IID)and non-IID datasets on Raspberry Pis.The experiment results suggest that the FedStrag outperforms the baseline FedAvg in all possible cases.
基金supported and funded by theDeanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-RP23082).
文摘Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G applications.Artificial Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and reliability.In this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task completion.However,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource wastage.Additionally,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities problem.This paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH scenarios.Additionally,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH scenarios.The performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed approach.The simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.
基金supported by the National Research Foundation of Korea(NRF)funded by theMinistry of Science and ICT(2022K1A3A1A61014825)。
文摘With the rapid advancement of ICT and IoT technologies,the integration of Edge and Fog Computing has become essential to meet the increasing demands for real-time data processing and network efficiency.However,these technologies face critical security challenges,exacerbated by the emergence of quantum computing,which threatens traditional encryption methods.The rise in cyber-attacks targeting IoT and Edge/Fog networks underscores the need for robust,quantum-resistant security solutions.To address these challenges,researchers are focusing on Quantum Key Distribution and Post-Quantum Cryptography,which utilize quantum-resistant algorithms and the principles of quantum mechanics to ensure data confidentiality and integrity.This paper reviews the current security practices in IoT and Edge/Fog environments,explores the latest advancements in QKD and PQC technologies,and discusses their integration into distributed computing systems.Additionally,this paper proposes an enhanced QKD protocol combining the Cascade protocol and Kyber algorithm to address existing limitations.Finally,we highlight future research directions aimed at improving the scalability,efficiency,and practicality of QKD and PQC for securing IoT and Edge/Fog networks against evolving quantum threats.
基金funded by Researchers Supporting Project Number(RSPD2025R947)King Saud University,Riyadh,Saudi Arabia.
文摘Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resource bottlenecks and increased energy consumption.This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management(QIARM)model,which introduces novel algorithms inspired by quantum principles for enhanced resource allocation.QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically.In addition,an energy-aware scheduling module minimizes power consumption by selecting optimal configurations based on energy metrics.The simulation was carried out in a 360-minute environment with eight distinct scenarios.This study introduces a novel quantum-inspired resource management framework that achieves up to 98%task offload success and reduces energy consumption by 20%,addressing critical challenges of scalability and efficiency in dynamic fog computing environments.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2504).
文摘Vehicular networks enable seamless connectivity for exchanging emergency and infotainment content.However,retrieving infotainment data from remote servers often introduces high delays,degrading the Quality of Service(QoS).To overcome this,caching frequently requested content at fog-enabled Road Side Units(RSUs)reduces communication latency.Yet,the limited caching capacity of RSUs makes it impractical to store all contents with varying sizes and popularity.This research proposes an efficient content caching algorithm that adapts to dynamic vehicular demands on highways to maximize request satisfaction.The scheme is evaluated against Intelligent Content Caching(ICC)and Random Caching(RC).The obtained results show that our proposed scheme entertains more contentrequesting vehicles as compared to ICC and RC,with 33%and 41%more downloaded data in 28%and 35%less amount of time from ICC and RC schemes,respectively.
基金supported in part by the Natural Science Foundation of China (62171110,U19B2028 and U20B2070)。
文摘Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is considered,where multiple User Equipments(UEs)offload their computational tasks to the F-RAN through fog nodes.Each UE can select one of the fog nodes to offload its task,and each fog node may serve multiple UEs.The tasks are computed by the fog nodes or further offloaded to the cloud via a capacity-limited fronhaul link.In order to compute all UEs'tasks quickly,joint optimization of UE-Fog association,radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs.This min-max problem is formulated as a Mixed Integer Nonlinear Program(MINP).To tackle it,first,MINP is reformulated as a continuous optimization problem,and then the Majorization Minimization(MM)method is used to find a solution.The MM approach that we develop is unconventional in that each MM subproblem is solved inexactly with the same provable convergence guarantee as the exact MM,thereby reducing the complexity of MM iteration.In addition,a cooperative offloading model is considered,where the fog nodes compress-and-forward their received signals to the cloud.Under this model,a similar min-max latency optimization problem is formulated and tackled by the inexact MM.Simulation results show that the proposed algorithms outperform some offloading strategies,and that the cooperative offloading can exploit transmission diversity better than noncooperative offloading to achieve better latency performance.
基金supported by the National Natural Science Foundation of China under Grant No.62271040,and 62341102the Fundamental Research Funds for the Central Universities under Grant No.2023JBGP004.
文摘In the rapidly evolving landscape of Industry 4.0(I4.0),the convergence of information and operational technologies necessitates real-time communication and collaboration across cyber-physical systems and the Internet of Things(IoT).Rapid data transmission is particularly critical within enterprises(vertically)and among stakeholders(horizontally)in this complex,heterogeneous ecosystem.While current research has focused on data application,processing,and storage within the cloud-edge-device continuum,cross-edge transmission has received less attention,resulting in challenges such as suboptimal routing and excessive delays in horizontal communications.To address the above issues,this paper introduces a Connection-As-Required Scheme(CARS)specifically designed for delay-sensitive IoT and Cyber-Physical System(CPS)applications,where low-latency communication is essential for operational efficiency.CARS leverages Lyapunov optimization and backpressure algorithms to optimize traffic scheduling and routing,minimizing communication delay between entities.Benchmarking against state-of-the-art solutions,CARS reduces Round-Trip Time(RTT)to approximately 47.0%of conventional methods and decreases delay by 24.5%in TCP-based and 26.0%in UDP-based applications.These results highlight the potential of CARS to facilitate effective,low-latency collaboration in diverse I4.0 environments.
