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Multi-Objective Enhanced Cheetah Optimizer for Joint Optimization of Computation Offloading and Task Scheduling in Fog Computing 认领 引用
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作者 Ahmad Zia Nazia Azim +5 位作者 Bekarystankyzy Akbayan Khalid J.Alzahrani Ateeq Ur Rehman Faheem Ullah Khan Nouf Al-Kahtani Hend Khalid Alkahtani 《Computers, Materials & Continua》 SCIE EI 2026年第3期1559-1588,共30页
The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous c... The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods. 展开更多
关键词 Computation offloading task scheduling cheetah optimizer fog computing optimization resource allocation internet of things
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Physical-layer secure hybrid task scheduling and resource management for fog computing IoT networks 认领 引用 被引量：1
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作者 ZHANG Shibo GAO Hongyuan +1 位作者 SU Yumeng SUN Rongchen 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第5期1146-1160,共15页
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
关键词 fog computing Internet-of-Things(IoT) physical layer security hybrid task scheduling and resource management quantum galaxy-based search algorithm(QGSA)
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An Adaptive Firefly Algorithm for Dependent Task Scheduling in IoT-Fog Computing 认领 引用
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作者 Adil Yousif 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第3期2869-2892,共24页
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
关键词 Fog computing scheduling resource management firefly algorithm genetic algorithm ant colony optimization
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Blockchain and signcryption enabled asynchronous federated learning framework in fog computing 认领 引用 被引量：1
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作者 Zhou Zhou Youliang Tian +3 位作者 Jinbo Xiong Changgen Peng Jing Li Nan Yang 《Digital Communications and Networks》 SCIE EI CSCD 2025年第2期442-454,共13页
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
关键词 Blockchain Signcryption Federated learning Asynchronous Fog computing
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Quantum Inspired Adaptive Resource Management Algorithm for Scalable and Energy Efficient Fog Computing in Internet of Things(IoT) 认领 引用
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作者 Sonia Khan Naqash Younas +3 位作者 Musaed Alhussein Wahib Jamal Khan Muhammad Shahid Anwar Khursheed Aurangzeb 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第3期2641-2660,共20页
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
关键词 Quantum computing resource management energy efficiency fog computing Internet of Things
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EPRFL:An Efficient Privacy-Preserving and Robust Federated Learning Scheme for Fog Computing 认领 引用
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作者 Ke Zhijie Xie Yong +1 位作者 Syed Hamad Shirazi Li Haifeng 《China Communications》 SCIE EI CSCD 2025年第4期202-222,共21页
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
关键词 federated learning fog computing internet of things privacy-preserving robustness
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Securing Parked Vehicle Assisted Fog Computing With Blockchain and Optimal Smart Contract Design 认领 引用 被引量：13
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作者 Xumin Huang Dongdong Ye +1 位作者 Rong Yu Lei Shu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2020年第2期426-441,共16页
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
关键词 Blockchain parked vehicle smart contract Stackelberg game vehicular fog computing(VFC)
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Secure and Efficient Computing Resource Management in Blockchain-Based Vehicular Fog Computing 认领 引用 被引量：6
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作者 Ming Kong Junhui Zhao +1 位作者 Xiaoke Sun Yiwen Nie 《China Communications》 SCIE EI CSCD 2021年第4期115-125,共11页
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
关键词 blockchain vehicular fog computing resource management contract theory
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A Non-Cooperative Differential Game-Based Security Model in Fog Computing 认领 引用 被引量：4
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作者 Zhi Li Xianwei Zhou +2 位作者 Yanzhu Liu Haitao Xu Li Miao 《China Communications》 SCIE CSCD 2017年第1期180-189,共10页
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
关键词 fog computing differential game fog security feedback nash equilibrium
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A Key Management Scheme Based on Hypergraph for Fog Computing 认领 引用 被引量：2
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作者 Zhi Li Yanzhu Liu +3 位作者 Di Liu Chunyang Li Wei Cui Guanglin Hu 《China Communications》 SCIE CSCD 2018年第11期158-170,共13页
Fog computing is a new paradigm supporting the stringent requirements of mobility applications by bridging cloud computing and smart devices. Since the smart devices may be deployed in dynamic areas where are out of s... Fog computing is a new paradigm supporting the stringent requirements of mobility applications by bridging cloud computing and smart devices. Since the smart devices may be deployed in dynamic areas where are out of strict monitoring and protection, fog computing requires security protections to ensure confidentiality and integrity. In this article, to deal with security requirements and considering the distinctive features, a key management based on hypergraph schemed is designed. Firstly, based on the key hypergraph, the three hierarchy architecture of fog computing is divided into two subnetworks. Furthermore, each key management process of both two subnetworks is designed to satisfy the operational and security requirements of fog computing. Finally, the performance evaluation and numerical simulation have been provided to validate the proposed scheme. 展开更多
关键词 fog computing fog security keymanagement hypergraph
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Fog Computing Dynamic Load Balancing Mechanism Based on Graph Repartitioning 认领 引用 被引量：10
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作者 SONG Ningning GONG Chao +1 位作者 AN Xingshuo ZHAN Qiang 《China Communications》 SCIE CSCD 2016年第3期156-164,共9页
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
关键词 fog computing graph partitioning load balancing
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SERVICES AND COMMUNICATIONS IN FOG COMPUTING 认领 引用 被引量：1
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作者 shangguang wang ao zhou +1 位作者 michael m.komarov stephen s.yau 《China Communications》 SCIE CSCD 2017年第11期I0001-I0002,共2页
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
关键词 SERVICES COMMUNICATIONS FOG COMPUTING
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RT-Notification: A Novel Real-Time Notification Protocol for Wireless Control in Fog Computing 认领 引用
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作者 Li Feng Jie Yang Huan Zhang 《China Communications》 SCIE CSCD 2017年第11期17-28,共12页
Fog computing is an emerging paradigm that has broad applications including storage, measurement and control. In this paper, we propose a novel real-time notification protocol called RT-Notification for wireless contr... Fog computing is an emerging paradigm that has broad applications including storage, measurement and control. In this paper, we propose a novel real-time notification protocol called RT-Notification for wireless control in fog computing. RT-Notification provides low-latency TDMA communication between an access point in Fog and a large number of portable monitoring devices equipped with sensor and actuator. RT-Notification differentiates two types of controls: urgent downlink actuator-oriented control and normal uplink access & scheduling control. Different from existing protocols, RT-Notification has two salient features:(i) support real-time notification of control frames, while not interrupting ongoing other transmissions, and(ii) support on-demand channel allocation for normal uplink access & scheduling control. RT-Notification can be implemented based on the commercial off-the-shelf 802.11 hardware. Our extensive simulations verify that RT-Notification is very effective in supporting the above two features. 展开更多
关键词 fog computing wireless control real-time notification
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A Data Download Method from RSUs Using Fog Computing in Connected Vehicles 认领 引用
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作者 Dae-Young Kim Seokhoon Kim 《Computers, Materials & Continua》 SCIE EI 2019年第5期375-387,共13页
Communication is important for providing intelligent services in connected vehicles.Vehicles must be able to communicate with different places and exchange information while driving.For service operation,connected veh... Communication is important for providing intelligent services in connected vehicles.Vehicles must be able to communicate with different places and exchange information while driving.For service operation,connected vehicles frequently attempt to download large amounts of data.They can request data downloading to a road side unit(RSU),which provides infrastructure for connected vehicles.The RSU is a data bottleneck in a transportation system because data traffic is concentrated on the RSU.Therefore,it is not appropriate for a connected vehicle to always attempt a high speed download from the RSU.If the mobile network between a connected vehicle and an RSU has poor connection quality,the efficiency and speed of the data download from the RSU is decreased.This problem affects the quality of the user experience.Therefore,it is important for a connected vehicle to connect to an RSU with consideration of the network conditions in order to try to maximize download speed.The proposed method maximizes download speed from an RSU using a machine learning algorithm.To collect and learn from network data,fog computing is used.A fog server is integrated with the RSU to perform computing.If the algorithm recognizes that conditions are not good for mass data download,it will not attempt to download at high speed.Thus,the proposed method can improve the efficiency of high speed downloads.This conclusion was validated using extensive computer simulations. 展开更多
关键词 Connected car fog computing data download DSRC machine learning
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Enabling intelligence in fog computing to achieve energy and latency reduction 认领 引用 被引量：9
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作者 Quang Duy La Mao V.Ngo +2 位作者 Thinh Quang Dinh Tony Q.S.Quek Hyundong Shin 《Digital Communications and Networks》 SCIE 2019年第1期3-9,共7页
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
关键词 Fog computing Edge computing Machine learning MAC scheduling Computational offloading Energy efficiency
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Task Offloading Decision in Fog Computing System 认领 引用 被引量：8
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作者 Qiliang Zhu Baojiang Si +1 位作者 Feifan Yang You Ma 《China Communications》 SCIE CSCD 2017年第11期59-68,共10页
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
关键词 fog computing task offioading energy consumption execution time
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Mobile Fog Computing by Using SDN/NFV on 5G Edge Nodes 认领 引用 被引量：4
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作者 G.R.Sreekanth S.Ahmed Najat Ahmed +3 位作者 Marko Sarac Ivana Strumberger Nebojsa Bacanin Miodrag Zivkovic 《Computer Systems Science & Engineering》 SCIE EI 2022年第5期751-765,共15页
Abstract:Fog computing provides quality of service for cloud infrastructure.As the data computation intensifies,edge computing becomes difficult.Therefore,mobile fog computing is used for reducing traffic and the time... Abstract:Fog computing provides quality of service for cloud infrastructure.As the data computation intensifies,edge computing becomes difficult.Therefore,mobile fog computing is used for reducing traffic and the time for data computation in the network.In previous studies,software-defined networking(SDN)and network functions virtualization(NFV)were used separately in edge computing.Current industrial and academic research is tackling to integrate SDN and NFV in different environments to address the challenges in performance,reliability,and scalability.SDN/NFV is still in development.The traditional Internet of things(IoT)data analysis system is only based on a linear and time-variant system that needs an IoT data system with a high-precision model.This paper proposes a combined architecture of SDN and NFV on an edge node server for IoT devices to reduce the computational complexity in cloud-based fog computing.SDN provides a generalization structure of the forwarding plane,which is separated from the control plane.Meanwhile,NFV concentrates on virtualization by combining the forwarding model with virtual network functions(VNFs)as a single or chain of VNFs,which leads to interoperability and consistency.The orchestrator layer in the proposed software-defined NFV is responsible for handling real-time tasks by using an edge node server through the SDN controller via four actions:task creation,modification,operation,and completion.Our proposed architecture is simulated on the EstiNet simulator,and total time delay,reliability,and satisfaction are used as evaluation parameters.The simulation results are compared with the results of existing architectures,such as software-defined unified virtual monitoring function and ASTP,to analyze the performance of the proposed architecture.The analysis results indicate that our proposed architecture achieves better performance in terms of total time delay(1800 s for 200 IoT devices),reliability(90%),and satisfaction(90%). 展开更多
关键词 Mobile fog computing edge computing edge node IoT softwaredefined networking network functions virtualization orchestrator
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An Efficient Impersonation Attack Detection Method in Fog Computing 认领 引用 被引量：4
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作者 Jialin Wan Muhammad Waqas +4 位作者 Shanshan Tu Syed Mudassir Hussain Ahsan Shah Sadaqat Ur Rehman Muhammad Hanif 《Computers, Materials & Continua》 SCIE EI 2021年第7期267-281,共15页
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
关键词 Fog computing double Sarsa attack detection physical layer key security
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A Broad Learning-Driven Network Traffic Analysis System Based on Fog Computing Paradigm 认领 引用 被引量：3
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作者 Xiting Peng Kaoru Ota Mianxiong Dong 《China Communications》 SCIE CSCD 2020年第2期1-13,共13页
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
关键词 traffic analysis fog computing broad learning radio access networks
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Integration of Fog Computing for Health Record Management Using Blockchain Technology 认领 引用 被引量：1
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作者 Mesfer AI Duhayyim Fahd N.Al-Wesabi +5 位作者 Radwa Marzouk Abdalla Ibrahim Abdalla Musa Noha Negm Anwer Mustafa Hilal Manar Ahmed Hamza Mohammed Rizwanullah 《Computers, Materials & Continua》 SCIE EI 2022年第5期4135-4149,共15页
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
关键词 Fog computing IoMT block chain YAC security IoT consensus protocol
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