With the advancements of the next-generation communication networking and Internet ofThings(IoT)technologies,a variety of computation-intensive applications(e.g.,autonomous driving and face recognition)have emerged.Th...With the advancements of the next-generation communication networking and Internet ofThings(IoT)technologies,a variety of computation-intensive applications(e.g.,autonomous driving and face recognition)have emerged.The execution of these IoT applications demands a lot of computing resources.Nevertheless,terminal devices(TDs)usually do not have sufficient computing resources to process these applications.Offloading IoT applications to be processed by mobile edge computing(MEC)servers with more computing resources provides a promising way to address this issue.While a significant number of works have studied task offloading,only a few of them have considered the security issue.This study investigates the problem of spectrum allocation and security-sensitive task offloading in an MEC system.Dynamic voltage scaling(DVS)technology is applied by TDs to reduce energy consumption and computing time.To guarantee data security during task offloading,we use AES cryptographic technique.The studied problem is formulated as an optimization problem and solved by our proposed efficient offloading scheme.The simulation results show that the proposed scheme can reduce system cost while guaranteeing data security.展开更多
Applying non-orthogonal multiple access(NOMA)to the mobile edge computing(MEC)network supported by unmanned aerial vehicles(UAVs)can improve spectral efficiency and achieve massive user access on the basis of solving ...Applying non-orthogonal multiple access(NOMA)to the mobile edge computing(MEC)network supported by unmanned aerial vehicles(UAVs)can improve spectral efficiency and achieve massive user access on the basis of solving computing resource constraints and coverage problems.However,the UAV-enabled network has a serious risk of information leakage on account of the openness of wireless channel.This paper considers a UAV-MEC secure network based on NOMA technology,which aims to minimize the UAV energy consumption.To achieve the purpose while meeting the security and users’latency requirements,we formulate an optimization problem that jointly optimizes the UAV trajectory and the allocation of network resources.Given that the original problem is non-convex and multivariate coupled,we proposed an effective algorithm to decouple the nonconvex problem into independent user relation coefficients and subproblems based on successive convex approximation(SCA)and block coordinate descent(BCD).The simulation results showcase the performance of our optimization scheme across various parameter settings and confirm its superiority over other benchmarks with respect to energy consumption.展开更多
在任务计算密集型和延迟敏感型的场景下,无人机辅助的移动边缘计算由于其高机动性和放置成本低的特点而被广泛研究.然而,无人机的能耗限制导致其无法长时间工作并且卸载任务内的不同模块往往存在着依赖关系.针对这种情况,以有向无环图(d...在任务计算密集型和延迟敏感型的场景下,无人机辅助的移动边缘计算由于其高机动性和放置成本低的特点而被广泛研究.然而,无人机的能耗限制导致其无法长时间工作并且卸载任务内的不同模块往往存在着依赖关系.针对这种情况,以有向无环图(direct acyclic graph,DAG)为基础对任务内部模块的依赖关系进行建模,综合考虑系统时延和能耗的影响,以最小化系统成本为优化目标得到最优的卸载策略.为了解决这一优化问题,提出了一种基于亚群、高斯变异和反向学习的二进制灰狼优化算法(binary grey wolf optimization algorithm based on subpopulation,Gaussian mutation,and reverse learning,BGWOSGR).仿真结果表明,所提出算法计算出的系统成本比其他4种对比方法分别降低了约19%、27%、16%、13%,并且收敛速度更快.展开更多
电力设备智能化运维需求不断增长的背景下,传统巡检方式存在效率低、时效差等问题,难以满足现代电网对精细化管理和实时感知的要求。为了解决这一问题,文章分析建筑信息模型(Building Information Modeling,BIM)与5G-移动边缘运算(5G-Mo...电力设备智能化运维需求不断增长的背景下,传统巡检方式存在效率低、时效差等问题,难以满足现代电网对精细化管理和实时感知的要求。为了解决这一问题,文章分析建筑信息模型(Building Information Modeling,BIM)与5G-移动边缘运算(5G-Mobile Edge Computing,5G-MEC)技术原理,构建基于BIM与5G-MEC融合的电源设备三维可视化智能巡检系统,并开展系统部署与实验验证,实验结果表明系统具备良好的实时响应与智能识别能力,具有较高的应用价值。展开更多
随着互联网技术的发展,云游戏、虚拟现实和互动直播等新兴交互式多媒体应用引起了广泛关注。当前智能设备的计算能力难以满足多媒体内容对超高渲染和实时交互的需求,且云端赋能方式因存在高带宽、高延迟、高能耗等问题,限制了其在移动...随着互联网技术的发展,云游戏、虚拟现实和互动直播等新兴交互式多媒体应用引起了广泛关注。当前智能设备的计算能力难以满足多媒体内容对超高渲染和实时交互的需求,且云端赋能方式因存在高带宽、高延迟、高能耗等问题,限制了其在移动网络中的实际应用。为应对这些挑战,提出一种边缘计算辅助交互式多媒体应用的系统框架,旨在确保满足用户服务质量需求的前提下降低系统能耗。构建融合非正交多址(Non-Orthogonal Multiple Access,NOMA)与移动边缘计算(Mobile Edge Computing,MEC)技术的网络通信模型,考虑到MEC服务器资源受限以及用户服务质量需求各异等因素,提出联合用户关联和资源分配的优化方案。为高效解决优化问题,结合遗传算法(Genetic Algorithm,GA)和粒子群优化(Particle Swarm Optimization,PSO)的优势,设计了分层自适应搜索算法(Hierarchical GA and PSO Based Adaptive Search Algorithm,HGPASA)。通过一系列仿真实验,充分验证了所提算法的有效性。展开更多
基金supported in part by Key Scientific Research Projects of Colleges and Universities in Anhui Province(2022AH051921)Science Research Project of Bengbu University(2024YYX47pj,2024YYX48pj)+8 种基金Anhui Province Excellent Research and Innovation Team in Intelligent Manufacturing and Information Technology(2023AH052938)Big Data and Machine Learning Research Team(BBXYKYTDxj05)Funding Project for the Cultivation of Outstanding Talents in Colleges and Universities(gxyqZD2021135)the Key Scientific Research Projects of Anhui Provincial Department of Education(2022AH051376)Start Up Funds for Scientific Research of High-Level Talents of Bengbu University(BBXY2020KYQD02)Scientific Research and Development Fund of Suzhou University(2021fzjj29)Research on Grain Logistics Data Processing and Safety Issues(ALAQ202401017)the Open Fund of State Key Laboratory of Tea Plant Biology and Utilization(SKLTOF20220131)funded by the Ongoing Research Funding Program(ORF-2025-102),King Saud University,Riyadh,Saudi Arabia.
文摘With the advancements of the next-generation communication networking and Internet ofThings(IoT)technologies,a variety of computation-intensive applications(e.g.,autonomous driving and face recognition)have emerged.The execution of these IoT applications demands a lot of computing resources.Nevertheless,terminal devices(TDs)usually do not have sufficient computing resources to process these applications.Offloading IoT applications to be processed by mobile edge computing(MEC)servers with more computing resources provides a promising way to address this issue.While a significant number of works have studied task offloading,only a few of them have considered the security issue.This study investigates the problem of spectrum allocation and security-sensitive task offloading in an MEC system.Dynamic voltage scaling(DVS)technology is applied by TDs to reduce energy consumption and computing time.To guarantee data security during task offloading,we use AES cryptographic technique.The studied problem is formulated as an optimization problem and solved by our proposed efficient offloading scheme.The simulation results show that the proposed scheme can reduce system cost while guaranteeing data security.
基金supported in part by the National Natural Science Foundation of China under Grant 61971474in part by the National Natural Science Foundation of China under Grant 62301594+2 种基金in part by the Special Funds of the National Natural Science Foundation of China under Grant 62341112in part by the Beijing Nova Program under Grant Z201100006820121in part by the Beijing Municipal Science and Technology Project under Grant Z181100003218015.
文摘Applying non-orthogonal multiple access(NOMA)to the mobile edge computing(MEC)network supported by unmanned aerial vehicles(UAVs)can improve spectral efficiency and achieve massive user access on the basis of solving computing resource constraints and coverage problems.However,the UAV-enabled network has a serious risk of information leakage on account of the openness of wireless channel.This paper considers a UAV-MEC secure network based on NOMA technology,which aims to minimize the UAV energy consumption.To achieve the purpose while meeting the security and users’latency requirements,we formulate an optimization problem that jointly optimizes the UAV trajectory and the allocation of network resources.Given that the original problem is non-convex and multivariate coupled,we proposed an effective algorithm to decouple the nonconvex problem into independent user relation coefficients and subproblems based on successive convex approximation(SCA)and block coordinate descent(BCD).The simulation results showcase the performance of our optimization scheme across various parameter settings and confirm its superiority over other benchmarks with respect to energy consumption.
文摘在任务计算密集型和延迟敏感型的场景下,无人机辅助的移动边缘计算由于其高机动性和放置成本低的特点而被广泛研究.然而,无人机的能耗限制导致其无法长时间工作并且卸载任务内的不同模块往往存在着依赖关系.针对这种情况,以有向无环图(direct acyclic graph,DAG)为基础对任务内部模块的依赖关系进行建模,综合考虑系统时延和能耗的影响,以最小化系统成本为优化目标得到最优的卸载策略.为了解决这一优化问题,提出了一种基于亚群、高斯变异和反向学习的二进制灰狼优化算法(binary grey wolf optimization algorithm based on subpopulation,Gaussian mutation,and reverse learning,BGWOSGR).仿真结果表明,所提出算法计算出的系统成本比其他4种对比方法分别降低了约19%、27%、16%、13%,并且收敛速度更快.
文摘电力设备智能化运维需求不断增长的背景下,传统巡检方式存在效率低、时效差等问题,难以满足现代电网对精细化管理和实时感知的要求。为了解决这一问题,文章分析建筑信息模型(Building Information Modeling,BIM)与5G-移动边缘运算(5G-Mobile Edge Computing,5G-MEC)技术原理,构建基于BIM与5G-MEC融合的电源设备三维可视化智能巡检系统,并开展系统部署与实验验证,实验结果表明系统具备良好的实时响应与智能识别能力,具有较高的应用价值。
文摘随着互联网技术的发展,云游戏、虚拟现实和互动直播等新兴交互式多媒体应用引起了广泛关注。当前智能设备的计算能力难以满足多媒体内容对超高渲染和实时交互的需求,且云端赋能方式因存在高带宽、高延迟、高能耗等问题,限制了其在移动网络中的实际应用。为应对这些挑战,提出一种边缘计算辅助交互式多媒体应用的系统框架,旨在确保满足用户服务质量需求的前提下降低系统能耗。构建融合非正交多址(Non-Orthogonal Multiple Access,NOMA)与移动边缘计算(Mobile Edge Computing,MEC)技术的网络通信模型,考虑到MEC服务器资源受限以及用户服务质量需求各异等因素,提出联合用户关联和资源分配的优化方案。为高效解决优化问题,结合遗传算法(Genetic Algorithm,GA)和粒子群优化(Particle Swarm Optimization,PSO)的优势,设计了分层自适应搜索算法(Hierarchical GA and PSO Based Adaptive Search Algorithm,HGPASA)。通过一系列仿真实验,充分验证了所提算法的有效性。
