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 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
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
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
Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems...Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems are susceptible to malicious eavesdropping attacks during the information transmission,and this issue has not been adequately addressed.In this paper,we propose a physical-layer secure fog computing IoT system model,which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers.The secrecy rate of the proposed model is analyzed,and the quantum galaxy–based search algorithm(QGSA)is proposed to solve the hybrid task scheduling and resource management problem of the network.The computational complexity and convergence of the proposed algorithm are analyzed.Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks.Moreover,the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios.展开更多
Fog computing is an emerging paradigm of cloud computing which to meet the growing computation demand of mobile application. It can help mobile devices to overcome resource constraints by offloading the computationall...Fog computing is an emerging paradigm of cloud computing which to meet the growing computation demand of mobile application. It can help mobile devices to overcome resource constraints by offloading the computationally intensive tasks to cloud servers. The challenge of the cloud is to minimize the time of data transfer and task execution to the user, whose location changes owing to mobility, and the energy consumption for the mobile device. To provide satisfactory computation performance is particularly challenging in the fog computing environment. In this paper, we propose a novel fog computing model and offloading policy which can effectively bring the fog computing power closer to the mobile user. The fog computing model consist of remote cloud nodes and local cloud nodes, which is attached to wireless access infrastructure. And we give task offloading policy taking into account executi+on, energy consumption and other expenses. We finally evaluate the performance of our method through experimental simulations. The experimental results show that this method has a significant effect on reducing the execution time of tasks and energy consumption of mobile devices.展开更多
Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation,communication,storage,and analy...Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation,communication,storage,and analytics closer to the End Users(EUs).In order to address the issues of energy efficiency and latency requirements for the time-critical Internet-of-Things(IoT)applications,fog computing systems could apply intelligence features in their operations to take advantage of the readily available data and computing resources.In this paper,we propose an approach that involves device-driven and human-driven intelligence as key enablers to reduce energy consumption and latency in fog computing via two case studies.The first one makes use of the machine learning to detect user behaviors and perform adaptive low-latency Medium Access Control(MAC)-layer scheduling among sensor devices.In the second case study on task offloading,we design an algorithm for an intelligent EU device to select its offloading decision in the presence of multiple fog nodes nearby,at the same time,minimize its own energy and latency objectives.Our results show a huge but untapped potential of intelligence in tackling the challenges of fog computing.展开更多
In vehicular fog computing(VFC),the resource transactions in the Internet of Vehicles(IoV)have become a novel resource management scheme that can improve system resource utilization and the quality of vehicle services...In vehicular fog computing(VFC),the resource transactions in the Internet of Vehicles(IoV)have become a novel resource management scheme that can improve system resource utilization and the quality of vehicle services.In this paper,in order to improve the security and fairness of resource transactions,we design a blockchain-based resource management scheme for VFC.First,we propose the concept of resource coin(RC)and develop a blockchain-based secure computing reource trading mechanism in terms of RC.As a node of the blockchain network,the roadside unit(RSU)participates in verifying the legitimacy of transactions and the creation of new blocks.Next,we propose a resource management scheme based on contract theory,encouraging parked vehicles to contribute computing resource so that RSU could complete proof of work(PoW)quickly,improve the success probability of block creation and get RC rewards.We use the gradient descent method to solve the computing resource utilization that can maximize the RC revenue of RSUs and vehicles during the block creation.Finally,the performance of this model is validated in simulation result and analysis.展开更多
Vehicular fog computing(VFC)has been envisioned as an important application of fog computing in vehicular networks.Parked vehicles with embedded computation resources could be exploited as a supplement for VFC.They co...Vehicular fog computing(VFC)has been envisioned as an important application of fog computing in vehicular networks.Parked vehicles with embedded computation resources could be exploited as a supplement for VFC.They cooperate with fog servers to process offloading requests at the vehicular network edge,leading to a new paradigm called parked vehicle assisted fog computing(PVFC).However,each coin has two sides.There is a follow-up challenging issue in the distributed and trustless computing environment.The centralized computation offloading without tamper-proof audit causes security threats.It could not guard against false-reporting,free-riding behaviors,spoofing attacks and repudiation attacks.Thus,we leverage the blockchain technology to achieve decentralized PVFC.Request posting,workload undertaking,task evaluation and reward assignment are organized and validated automatically through smart contract executions.Network activities in computation offloading become transparent,verifiable and traceable to eliminate security risks.To this end,we introduce network entities and design interactive smart contract operations across them.The optimal smart contract design problem is formulated and solved within the Stackelberg game framework to minimize the total payments for users.Security analysis and extensive numerical results are provided to demonstrate that our scheme has high security and efficiency guarantee.展开更多
Fog computing is a new paradigm providing network services such as computing, storage between the end users and cloud. The distributed and open structure are the characteristics of fog computing, which make it vulnera...Fog computing is a new paradigm providing network services such as computing, storage between the end users and cloud. The distributed and open structure are the characteristics of fog computing, which make it vulnerable and very weak to security threats. In this article, the interaction between vulnerable nodes and malicious nodes in the fog computing is investigated as a non-cooperative differential game. The complex decision making process is reviewed and analyzed. To solve the game, a fictitious play-based algorithm is which the vulnerable node and the malicious nodes reach a feedback Nash equilibrium. We attain optimal strategy of energy consumption with Qo S guarantee for the system, which are conveniently operated and suitable for fog nodes. The system simulation identifies the propagation of malicious nodes. We also determine the effects of various parameters on the optimal strategy. The simulation results support a theoretical foundation to limit malicious nodes in fog computing, which can help fog service providers make the optimal dynamic strategies when different types of nodes dynamically change their strategies.展开更多
Fog computing paradigm extends computing,communication,storage,and network resources to the network’s edge.As the fog layer is located between cloud and end-users,it can provide more convenience and timely services t...Fog computing paradigm extends computing,communication,storage,and network resources to the network’s edge.As the fog layer is located between cloud and end-users,it can provide more convenience and timely services to end-users.However,in fog computing(FC),attackers can behave as real fog nodes or end-users to provide malicious services in the network.The attacker acts as an impersonator to impersonate other legitimate users.Therefore,in this work,we present a detection technique to secure the FC environment.First,we model a physical layer key generation based on wireless channel characteristics.To generate the secret keys between the legitimate users and avoid impersonators,we then consider a Double Sarsa technique to identify the impersonators at the receiver end.We compare our proposed Double Sarsa technique with the other two methods to validate our work,i.e.,Sarsa and Q-learning.The simulation results demonstrate that the method based on Double Sarsa outperforms Sarsa and Q-learning approaches in terms of false alarm rate(FAR),miss detection rate(MDR),and average error rate(AER).展开更多
The development of communication technologies which support traffic-intensive applications presents new challenges in designing a real-time traffic analysis architecture and an accurate method that suitable for a wide...The development of communication technologies which support traffic-intensive applications presents new challenges in designing a real-time traffic analysis architecture and an accurate method that suitable for a wide variety of traffic types.Current traffic analysis methods are executed on the cloud,which needs to upload the traffic data.Fog computing is a more promising way to save bandwidth resources by offloading these tasks to the fog nodes.However,traffic analysis models based on traditional machine learning need to retrain all traffic data when updating the trained model,which are not suitable for fog computing due to the poor computing power.In this study,we design a novel fog computing based traffic analysis system using broad learning.For one thing,fog computing can provide a distributed architecture for saving the bandwidth resources.For another,we use the broad learning to incrementally train the traffic data,which is more suitable for fog computing because it can support incremental updates of models without retraining all data.We implement our system on the Raspberry Pi,and experimental results show that we have a 98%probability to accurately identify these traffic data.Moreover,our method has a faster training speed compared with Convolutional Neural Network(CNN).展开更多
Efficient and effective data acquisition is of theoretical and practical importance in WSN applications because data measured and collected by WSN is often unreliable, such as those often accompanied by noise and erro...Efficient and effective data acquisition is of theoretical and practical importance in WSN applications because data measured and collected by WSN is often unreliable, such as those often accompanied by noise and error, missing values or inconsistent data. Motivated by fog computing, which focuses on how to effectively offload computation-intensive tasks from resource-constrained devices, this paper proposes a simple but yet effective data acquisition approach with the ability of filtering abnormal data and meeting the real-time requirement. Our method uses a cooperation mechanism by leveraging on both an architectural and algorithmic approach. Firstly, the sensor node with the limited computing resource only accomplishes detecting and marking the suspicious data using a light weight algorithm. Secondly, the cluster head evaluates suspicious data by referring to the data from the other sensor nodes in the same cluster and discard the abnormal data directly. Thirdly, the sink node fills up the discarded data with an approximate value using nearest neighbor data supplement method. Through the architecture, each node only consumes a few computational resources and distributes the heavily computing load to several nodes. Simulation results show that our data acquisition method is effective considering the real-time outlier filtering and the computing overhead.展开更多
Internet of Vehicles(IoV)is a new style of vehicular ad hoc network that is used to connect the sensors of each vehicle with each other and with other vehicles’sensors through the internet.These sensors generate diff...Internet of Vehicles(IoV)is a new style of vehicular ad hoc network that is used to connect the sensors of each vehicle with each other and with other vehicles’sensors through the internet.These sensors generate different tasks that should be analyzed and processed in some given period of time.They send the tasks to the cloud servers but these sending operations increase bandwidth consumption and latency.Fog computing is a simple cloud at the network edge that is used to process the jobs in a short period of time instead of sending them to cloud computing facilities.In some situations,fog computing cannot execute some tasks due to lack of resources.Thus,in these situations it transfers them to cloud computing that leads to an increase in latency and bandwidth occupation again.Moreover,several fog servers may be fuelled while other servers are empty.This implies an unfair distribution of jobs.In this research study,we shall merge the software defined network(SDN)with IoV and fog computing and use the parked vehicle as assistant fog computing node.This can improve the capabilities of the fog computing layer and help in decreasing the number of migrated tasks to the cloud servers.This increases the ratio of time sensitive tasks that meet the deadline.In addition,a new load balancing strategy is proposed.It works proactively to balance the load locally and globally by the local fog managers and SDN controller,respectively.The simulation experiments show that the proposed system is more efficient than VANET-Fog-Cloud and IoV-Fog-Cloud frameworks in terms of average response time and percentage of bandwidth consumption,meeting the deadline,and resource utilization.展开更多
Rogue nodes broadcasting false information in beacon messages may lead to catastrophic consequences in Vehicular Ad Hoc Networks(VANETs).Previous researchers used cryptography,trust scores,or past vehicle data to dete...Rogue nodes broadcasting false information in beacon messages may lead to catastrophic consequences in Vehicular Ad Hoc Networks(VANETs).Previous researchers used cryptography,trust scores,or past vehicle data to detect rogue nodes;however,these methods suffer from high processing delay,overhead,and False–Positive Rate(FPR).We propose herein Greenshield's traffic model–based fog computing scheme called Fog–based Rogue Node Detection(F–RouND),which dynamically utilizes the On–Board Units(OBUs)of all vehicles in the region for rogue node detection.We aim to reduce the data processing delays and FPR in detecting rogue nodes at high vehicle densities.The performance of the F–RouND framework was evaluated via simulations.Results show that the F–RouND framework ensures 45%lower processing delays,12%lower overhead,and 36%lower FPR at the urban scenario than the existing rogue node detection schemes even when the number of rogue nodes increases by up to 40%in the region.展开更多
Because of cloud computing's high degree of polymerization calculation mode, it can't give full play to the resources of the edge device such as computing, storage, etc. Fog computing can improve the resource ...Because of cloud computing's high degree of polymerization calculation mode, it can't give full play to the resources of the edge device such as computing, storage, etc. Fog computing can improve the resource utilization efficiency of the edge device, and solve the problem about service computing of the delay-sensitive applications. This paper researches on the framework of the fog computing, and adopts Cloud Atomization Technology to turn physical nodes in different levels into virtual machine nodes. On this basis, this paper uses the graph partitioning theory to build the fog computing's load balancing algorithm based on dynamic graph partitioning. The simulation results show that the framework of the fog computing after Cloud Atomization can build the system network flexibly, and dynamic load balancing mechanism can effectively configure system resources as well as reducing the consumption of node migration brought by system changes.展开更多
Fog Computing(FC)provides processing and storage resources at the edge of the Internet of Things(IoT).By doing so,FC can help reduce latency and improve reliability of IoT networks.The energy consumption of servers an...Fog Computing(FC)provides processing and storage resources at the edge of the Internet of Things(IoT).By doing so,FC can help reduce latency and improve reliability of IoT networks.The energy consumption of servers and computing resources is one of the factors that directly affect conservation costs in fog environments.Energy consumption can be reduced by efficacious scheduling methods so that tasks are offloaded on the best possible resources.To deal with this problem,a binary model based on the combination of the Krill Herd Algorithm(KHA)and the Artificial Hummingbird Algorithm(AHA)is introduced as Binary KHA-AHA(BAHA-KHA).KHA is used to improve AHA.Also,the BAHA-KHA local optimal problem for task scheduling in FC environments is solved using the dynamic voltage and frequency scaling(DVFS)method.The Heterogeneous Earliest Finish Time(HEFT)method is used to discover the order of task flow execution.The goal of the BAHA-KHA model is to minimize the number of resources,the communication between dependent tasks,and reduce energy consumption.In this paper,the FC environment is considered to address the workflow scheduling issue to reduce energy consumption and minimize makespan on fog resources.The results were tested on five different workflows(Montage,CyberShake,LIGO,SIPHT,and Epigenomics).The evaluations show that the BAHA-KHA model has the best performance in comparison with the AHA,KHA,PSO and GA algorithms.The BAHA-KHA model has reduced the makespan rate by about 18%and the energy consumption by about 24%in comparison with GA.This is a preview of subscription content,log in via an institution to check access.展开更多
Internet of Medical Things (IoMT) is a breakthrough technologyin the transfer of medical data via a communication system. Wearable sensordevices collect patient data and transfer them through mobile internet, thatis, ...Internet of Medical Things (IoMT) is a breakthrough technologyin the transfer of medical data via a communication system. Wearable sensordevices collect patient data and transfer them through mobile internet, thatis, the IoMT. Recently, the shift in paradigm from manual data storage toelectronic health recording on fog, edge, and cloud computing has been noted.These advanced computing technologies have facilitated medical services withminimum cost and available conditions. However, the IoMT raises a highconcern on network security and patient data privacy in the health caresystem. The main issue is the transmission of health data with high security inthe fog computing model. In today’s market, the best solution is blockchaintechnology. This technology provides high-end security and authenticationin storing and transferring data. In this research, a blockchain-based fogcomputing model is proposed for the IoMT. The proposed technique embedsa block chain with the yet another consensus (YAC) protocol building securityinfrastructure into fog computing for storing and transferring IoMT data inthe network. YAC is a consensus protocol that authenticates the input datain the block chain. In this scenario, the patients and their family membersare allowed to access the data. The empirical outcome of the proposedtechnique indicates high reliability and security against dangerous threats.The major advantages of using the blockchain model are high transparency,good traceability, and high processing speed. The technique also exhibitshigh reliability and efficiency in accessing data with secure transmission. Theproposed technique achieves 95% reliability in transferring a large number offiles up to 10,000.展开更多
Fog computing(FC)is a networking paradigm where wireless devices known as fog nodes are placed at the edge of the network(close to the Internet of Things(IoT)devices).Fog nodes provide services in lieu of the cloud.Th...Fog computing(FC)is a networking paradigm where wireless devices known as fog nodes are placed at the edge of the network(close to the Internet of Things(IoT)devices).Fog nodes provide services in lieu of the cloud.Thus,improving the performance of the network and making it attractive to social media-based systems.Security issues are one of the most challenges encountered in FC.In this paper,we propose an anomalybased Intrusion Detection and Prevention System(IDPS)against Man-in-theMiddle(MITM)attack in the fog layer.The system uses special nodes known as Intrusion Detection System(IDS)nodes to detect intrusion in the network.They periodically monitor the behavior of the fog nodes in the network.Any deviation from normal network activity is categorized as malicious,and the suspected node is isolated.ExponentiallyWeighted Moving Average(EWMA)is added to the system to smooth out the noise that is typically found in social media communications.Our results(with 95%confidence)show that the accuracy of the proposed system increases from 80%to 95%after EWMA is added.Also,with EWMA,the proposed system can detect the intrusion from 0.25–0.5 s seconds faster than that without EWMA.However,it affects the latency of services provided by the fog nodes by at least 0.75–1.3 s.Finally,EWMA has not increased the energy overhead of the system,due to its lightweight.展开更多
In the current cloud-based Internet-of-Things （IoT） model, smart devices （such as sensors, smartphones） exchange information through the Internet to cooperate and provide services to users, which could be citizens...In the current cloud-based Internet-of-Things （IoT） model, smart devices （such as sensors, smartphones） exchange information through the Internet to cooperate and provide services to users, which could be citizens, smart home systems, and industrial applications.展开更多
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 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 by the National Natural Science Foundation of China(61571149,62001139)the Initiation Fund for Postdoctoral Research in Heilongjiang Province(LBH-Q19098)the Natural Science Foundation of Heilongjiang Province(LH2020F0178).
文摘Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems are susceptible to malicious eavesdropping attacks during the information transmission,and this issue has not been adequately addressed.In this paper,we propose a physical-layer secure fog computing IoT system model,which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers.The secrecy rate of the proposed model is analyzed,and the quantum galaxy–based search algorithm(QGSA)is proposed to solve the hybrid task scheduling and resource management problem of the network.The computational complexity and convergence of the proposed algorithm are analyzed.Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks.Moreover,the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios.
基金supported by the NSFC (61602126)the scientific and technological project of Henan province (162102210214)
文摘Fog computing is an emerging paradigm of cloud computing which to meet the growing computation demand of mobile application. It can help mobile devices to overcome resource constraints by offloading the computationally intensive tasks to cloud servers. The challenge of the cloud is to minimize the time of data transfer and task execution to the user, whose location changes owing to mobility, and the energy consumption for the mobile device. To provide satisfactory computation performance is particularly challenging in the fog computing environment. In this paper, we propose a novel fog computing model and offloading policy which can effectively bring the fog computing power closer to the mobile user. The fog computing model consist of remote cloud nodes and local cloud nodes, which is attached to wireless access infrastructure. And we give task offloading policy taking into account executi+on, energy consumption and other expenses. We finally evaluate the performance of our method through experimental simulations. The experimental results show that this method has a significant effect on reducing the execution time of tasks and energy consumption of mobile devices.
文摘Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation,communication,storage,and analytics closer to the End Users(EUs).In order to address the issues of energy efficiency and latency requirements for the time-critical Internet-of-Things(IoT)applications,fog computing systems could apply intelligence features in their operations to take advantage of the readily available data and computing resources.In this paper,we propose an approach that involves device-driven and human-driven intelligence as key enablers to reduce energy consumption and latency in fog computing via two case studies.The first one makes use of the machine learning to detect user behaviors and perform adaptive low-latency Medium Access Control(MAC)-layer scheduling among sensor devices.In the second case study on task offloading,we design an algorithm for an intelligent EU device to select its offloading decision in the presence of multiple fog nodes nearby,at the same time,minimize its own energy and latency objectives.Our results show a huge but untapped potential of intelligence in tackling the challenges of fog computing.
基金This work was supported in part by the National Natural Science Foundation of China(U2001213,61971191 and 61661021)in part by the Beijing Natural Science Foundation under Grant L182018 and L201011,in part by National Key Research and Development Project(2020YFB1807204)+1 种基金in part by the open project of Shanghai Institute of Microsystem and Information Technology(20190910)in part by the Key project of Natural Science Foundation of Jiangxi Province(20202ACBL202006).
文摘In vehicular fog computing(VFC),the resource transactions in the Internet of Vehicles(IoV)have become a novel resource management scheme that can improve system resource utilization and the quality of vehicle services.In this paper,in order to improve the security and fairness of resource transactions,we design a blockchain-based resource management scheme for VFC.First,we propose the concept of resource coin(RC)and develop a blockchain-based secure computing reource trading mechanism in terms of RC.As a node of the blockchain network,the roadside unit(RSU)participates in verifying the legitimacy of transactions and the creation of new blocks.Next,we propose a resource management scheme based on contract theory,encouraging parked vehicles to contribute computing resource so that RSU could complete proof of work(PoW)quickly,improve the success probability of block creation and get RC rewards.We use the gradient descent method to solve the computing resource utilization that can maximize the RC revenue of RSUs and vehicles during the block creation.Finally,the performance of this model is validated in simulation result and analysis.
基金supported in part by the National Natural Science Foundation of China(61971148)the Science and Technology Program of Guangdong Province(2015B010129001)+2 种基金the Natural Science Foundation of Guangxi Province(2018GXNSFDA281013)the Foundation for Science and Technology Project of Guilin City(20190214-3)the Key Science and Technology Project of Guangxi(AA18242021)
文摘Vehicular fog computing(VFC)has been envisioned as an important application of fog computing in vehicular networks.Parked vehicles with embedded computation resources could be exploited as a supplement for VFC.They cooperate with fog servers to process offloading requests at the vehicular network edge,leading to a new paradigm called parked vehicle assisted fog computing(PVFC).However,each coin has two sides.There is a follow-up challenging issue in the distributed and trustless computing environment.The centralized computation offloading without tamper-proof audit causes security threats.It could not guard against false-reporting,free-riding behaviors,spoofing attacks and repudiation attacks.Thus,we leverage the blockchain technology to achieve decentralized PVFC.Request posting,workload undertaking,task evaluation and reward assignment are organized and validated automatically through smart contract executions.Network activities in computation offloading become transparent,verifiable and traceable to eliminate security risks.To this end,we introduce network entities and design interactive smart contract operations across them.The optimal smart contract design problem is formulated and solved within the Stackelberg game framework to minimize the total payments for users.Security analysis and extensive numerical results are provided to demonstrate that our scheme has high security and efficiency guarantee.
基金supported by the National Science Foundation Project of P. R. China (No. 61501026,61572072)Fundamental Research Funds for the Central Universities (No. FRF-TP-15-032A1)
文摘Fog computing is a new paradigm providing network services such as computing, storage between the end users and cloud. The distributed and open structure are the characteristics of fog computing, which make it vulnerable and very weak to security threats. In this article, the interaction between vulnerable nodes and malicious nodes in the fog computing is investigated as a non-cooperative differential game. The complex decision making process is reviewed and analyzed. To solve the game, a fictitious play-based algorithm is which the vulnerable node and the malicious nodes reach a feedback Nash equilibrium. We attain optimal strategy of energy consumption with Qo S guarantee for the system, which are conveniently operated and suitable for fog nodes. The system simulation identifies the propagation of malicious nodes. We also determine the effects of various parameters on the optimal strategy. The simulation results support a theoretical foundation to limit malicious nodes in fog computing, which can help fog service providers make the optimal dynamic strategies when different types of nodes dynamically change their strategies.
基金supported by Natural Science Foundation of China(61801008)The China National Key R&D Program(No.2018YFB0803600)+1 种基金Scientific Research Common Program of Beijing Municipal Commission of Education(No.KM201910005025)Chinese Postdoctoral Science Foundation(No.2020M670074).
文摘Fog computing paradigm extends computing,communication,storage,and network resources to the network’s edge.As the fog layer is located between cloud and end-users,it can provide more convenience and timely services to end-users.However,in fog computing(FC),attackers can behave as real fog nodes or end-users to provide malicious services in the network.The attacker acts as an impersonator to impersonate other legitimate users.Therefore,in this work,we present a detection technique to secure the FC environment.First,we model a physical layer key generation based on wireless channel characteristics.To generate the secret keys between the legitimate users and avoid impersonators,we then consider a Double Sarsa technique to identify the impersonators at the receiver end.We compare our proposed Double Sarsa technique with the other two methods to validate our work,i.e.,Sarsa and Q-learning.The simulation results demonstrate that the method based on Double Sarsa outperforms Sarsa and Q-learning approaches in terms of false alarm rate(FAR),miss detection rate(MDR),and average error rate(AER).
基金supported by JSPS KAKENHI Grant Number JP16K00117, JP19K20250KDDI Foundationthe China Scholarship Council (201808050016)
文摘The development of communication technologies which support traffic-intensive applications presents new challenges in designing a real-time traffic analysis architecture and an accurate method that suitable for a wide variety of traffic types.Current traffic analysis methods are executed on the cloud,which needs to upload the traffic data.Fog computing is a more promising way to save bandwidth resources by offloading these tasks to the fog nodes.However,traffic analysis models based on traditional machine learning need to retrain all traffic data when updating the trained model,which are not suitable for fog computing due to the poor computing power.In this study,we design a novel fog computing based traffic analysis system using broad learning.For one thing,fog computing can provide a distributed architecture for saving the bandwidth resources.For another,we use the broad learning to incrementally train the traffic data,which is more suitable for fog computing because it can support incremental updates of models without retraining all data.We implement our system on the Raspberry Pi,and experimental results show that we have a 98%probability to accurately identify these traffic data.Moreover,our method has a faster training speed compared with Convolutional Neural Network(CNN).
基金supported by National Natural Science Foundation of China, "Research on Accurate and Fair Service Recommendation Approach in Mobile Internet Environment", (No. 61571066)
文摘Efficient and effective data acquisition is of theoretical and practical importance in WSN applications because data measured and collected by WSN is often unreliable, such as those often accompanied by noise and error, missing values or inconsistent data. Motivated by fog computing, which focuses on how to effectively offload computation-intensive tasks from resource-constrained devices, this paper proposes a simple but yet effective data acquisition approach with the ability of filtering abnormal data and meeting the real-time requirement. Our method uses a cooperation mechanism by leveraging on both an architectural and algorithmic approach. Firstly, the sensor node with the limited computing resource only accomplishes detecting and marking the suspicious data using a light weight algorithm. Secondly, the cluster head evaluates suspicious data by referring to the data from the other sensor nodes in the same cluster and discard the abnormal data directly. Thirdly, the sink node fills up the discarded data with an approximate value using nearest neighbor data supplement method. Through the architecture, each node only consumes a few computational resources and distributes the heavily computing load to several nodes. Simulation results show that our data acquisition method is effective considering the real-time outlier filtering and the computing overhead.
文摘Internet of Vehicles(IoV)is a new style of vehicular ad hoc network that is used to connect the sensors of each vehicle with each other and with other vehicles’sensors through the internet.These sensors generate different tasks that should be analyzed and processed in some given period of time.They send the tasks to the cloud servers but these sending operations increase bandwidth consumption and latency.Fog computing is a simple cloud at the network edge that is used to process the jobs in a short period of time instead of sending them to cloud computing facilities.In some situations,fog computing cannot execute some tasks due to lack of resources.Thus,in these situations it transfers them to cloud computing that leads to an increase in latency and bandwidth occupation again.Moreover,several fog servers may be fuelled while other servers are empty.This implies an unfair distribution of jobs.In this research study,we shall merge the software defined network(SDN)with IoV and fog computing and use the parked vehicle as assistant fog computing node.This can improve the capabilities of the fog computing layer and help in decreasing the number of migrated tasks to the cloud servers.This increases the ratio of time sensitive tasks that meet the deadline.In addition,a new load balancing strategy is proposed.It works proactively to balance the load locally and globally by the local fog managers and SDN controller,respectively.The simulation experiments show that the proposed system is more efficient than VANET-Fog-Cloud and IoV-Fog-Cloud frameworks in terms of average response time and percentage of bandwidth consumption,meeting the deadline,and resource utilization.
文摘Rogue nodes broadcasting false information in beacon messages may lead to catastrophic consequences in Vehicular Ad Hoc Networks(VANETs).Previous researchers used cryptography,trust scores,or past vehicle data to detect rogue nodes;however,these methods suffer from high processing delay,overhead,and False–Positive Rate(FPR).We propose herein Greenshield's traffic model–based fog computing scheme called Fog–based Rogue Node Detection(F–RouND),which dynamically utilizes the On–Board Units(OBUs)of all vehicles in the region for rogue node detection.We aim to reduce the data processing delays and FPR in detecting rogue nodes at high vehicle densities.The performance of the F–RouND framework was evaluated via simulations.Results show that the F–RouND framework ensures 45%lower processing delays,12%lower overhead,and 36%lower FPR at the urban scenario than the existing rogue node detection schemes even when the number of rogue nodes increases by up to 40%in the region.
基金supported in part by the National Science and technology support program of P.R.China(No.2014BAH29F05)
文摘Because of cloud computing's high degree of polymerization calculation mode, it can't give full play to the resources of the edge device such as computing, storage, etc. Fog computing can improve the resource utilization efficiency of the edge device, and solve the problem about service computing of the delay-sensitive applications. This paper researches on the framework of the fog computing, and adopts Cloud Atomization Technology to turn physical nodes in different levels into virtual machine nodes. On this basis, this paper uses the graph partitioning theory to build the fog computing's load balancing algorithm based on dynamic graph partitioning. The simulation results show that the framework of the fog computing after Cloud Atomization can build the system network flexibly, and dynamic load balancing mechanism can effectively configure system resources as well as reducing the consumption of node migration brought by system changes.
文摘Fog Computing(FC)provides processing and storage resources at the edge of the Internet of Things(IoT).By doing so,FC can help reduce latency and improve reliability of IoT networks.The energy consumption of servers and computing resources is one of the factors that directly affect conservation costs in fog environments.Energy consumption can be reduced by efficacious scheduling methods so that tasks are offloaded on the best possible resources.To deal with this problem,a binary model based on the combination of the Krill Herd Algorithm(KHA)and the Artificial Hummingbird Algorithm(AHA)is introduced as Binary KHA-AHA(BAHA-KHA).KHA is used to improve AHA.Also,the BAHA-KHA local optimal problem for task scheduling in FC environments is solved using the dynamic voltage and frequency scaling(DVFS)method.The Heterogeneous Earliest Finish Time(HEFT)method is used to discover the order of task flow execution.The goal of the BAHA-KHA model is to minimize the number of resources,the communication between dependent tasks,and reduce energy consumption.In this paper,the FC environment is considered to address the workflow scheduling issue to reduce energy consumption and minimize makespan on fog resources.The results were tested on five different workflows(Montage,CyberShake,LIGO,SIPHT,and Epigenomics).The evaluations show that the BAHA-KHA model has the best performance in comparison with the AHA,KHA,PSO and GA algorithms.The BAHA-KHA model has reduced the makespan rate by about 18%and the energy consumption by about 24%in comparison with GA.This is a preview of subscription content,log in via an institution to check access.
文摘Internet of Medical Things (IoMT) is a breakthrough technologyin the transfer of medical data via a communication system. Wearable sensordevices collect patient data and transfer them through mobile internet, thatis, the IoMT. Recently, the shift in paradigm from manual data storage toelectronic health recording on fog, edge, and cloud computing has been noted.These advanced computing technologies have facilitated medical services withminimum cost and available conditions. However, the IoMT raises a highconcern on network security and patient data privacy in the health caresystem. The main issue is the transmission of health data with high security inthe fog computing model. In today’s market, the best solution is blockchaintechnology. This technology provides high-end security and authenticationin storing and transferring data. In this research, a blockchain-based fogcomputing model is proposed for the IoMT. The proposed technique embedsa block chain with the yet another consensus (YAC) protocol building securityinfrastructure into fog computing for storing and transferring IoMT data inthe network. YAC is a consensus protocol that authenticates the input datain the block chain. In this scenario, the patients and their family membersare allowed to access the data. The empirical outcome of the proposedtechnique indicates high reliability and security against dangerous threats.The major advantages of using the blockchain model are high transparency,good traceability, and high processing speed. The technique also exhibitshigh reliability and efficiency in accessing data with secure transmission. Theproposed technique achieves 95% reliability in transferring a large number offiles up to 10,000.
基金The Authors would like to acknowledge the support of King Fahd University of Petroleum and Minerals for this research.
文摘Fog computing(FC)is a networking paradigm where wireless devices known as fog nodes are placed at the edge of the network(close to the Internet of Things(IoT)devices).Fog nodes provide services in lieu of the cloud.Thus,improving the performance of the network and making it attractive to social media-based systems.Security issues are one of the most challenges encountered in FC.In this paper,we propose an anomalybased Intrusion Detection and Prevention System(IDPS)against Man-in-theMiddle(MITM)attack in the fog layer.The system uses special nodes known as Intrusion Detection System(IDS)nodes to detect intrusion in the network.They periodically monitor the behavior of the fog nodes in the network.Any deviation from normal network activity is categorized as malicious,and the suspected node is isolated.ExponentiallyWeighted Moving Average(EWMA)is added to the system to smooth out the noise that is typically found in social media communications.Our results(with 95%confidence)show that the accuracy of the proposed system increases from 80%to 95%after EWMA is added.Also,with EWMA,the proposed system can detect the intrusion from 0.25–0.5 s seconds faster than that without EWMA.However,it affects the latency of services provided by the fog nodes by at least 0.75–1.3 s.Finally,EWMA has not increased the energy overhead of the system,due to its lightweight.
文摘In the current cloud-based Internet-of-Things （IoT） model, smart devices （such as sensors, smartphones） exchange information through the Internet to cooperate and provide services to users, which could be citizens, smart home systems, and industrial applications.
