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
针对实时系统负载动态变化的问题,提出一种面向软实时的基于资源预留的反馈调度模型(Feedback Scheduling M odel based on Resource Reservation,FSM-RR),当负载发生变化时调整服务器的CPU带宽.接着针对混合任务提出一种自适应分层调...针对实时系统负载动态变化的问题,提出一种面向软实时的基于资源预留的反馈调度模型(Feedback Scheduling M odel based on Resource Reservation,FSM-RR),当负载发生变化时调整服务器的CPU带宽.接着针对混合任务提出一种自适应分层调度框架(Adaptive Hierarchical Scheduling Framework,AHSF),对不同类型任务采用相应的调度算法,保证硬实时任务在其截止期之前完成,同时尽可能的降低软实时以及非实时任务的截止期错失率.在RTSim平台上进行仿真实验,并与传统调度算法进行对比分析,实验结果表明本文提出调度算法具有较好的性能.展开更多
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the National Natural Science Foundation of China under Grant No.90412001 (国家自然科学基金)the National High-Tech Research and Development Plan of China under Grant No.2006AA02Z334 (国家高技术研究发展计划(863))the National Basic Research Program of China under Grant No.G2005CB321806 (国家重点基础研究发展计划(973))
文摘针对实时系统负载动态变化的问题,提出一种面向软实时的基于资源预留的反馈调度模型(Feedback Scheduling M odel based on Resource Reservation,FSM-RR),当负载发生变化时调整服务器的CPU带宽.接着针对混合任务提出一种自适应分层调度框架(Adaptive Hierarchical Scheduling Framework,AHSF),对不同类型任务采用相应的调度算法,保证硬实时任务在其截止期之前完成,同时尽可能的降低软实时以及非实时任务的截止期错失率.在RTSim平台上进行仿真实验,并与传统调度算法进行对比分析,实验结果表明本文提出调度算法具有较好的性能.
