In this paper,unmanned aerial vehicle(UAV)is adopted to serve as aerial base station(ABS)and mobile edge computing(MEC)platform for wire-less communication systems.When Internet of Things devices(IoTDs)cannot cope wit...In this paper,unmanned aerial vehicle(UAV)is adopted to serve as aerial base station(ABS)and mobile edge computing(MEC)platform for wire-less communication systems.When Internet of Things devices(IoTDs)cannot cope with computation-intensive and/or time-sensitive tasks,part of tasks is offloaded to the UAV side,and UAV process them with its own computing resources and caching resources.Thus,the burden of IoTDs gets relieved under the satisfaction of the quality of service(QoS)require-ments.However,owing to the limited resources of UAV,the cost of whole system,i.e.,that is defined as the weighted sum of energy consumption and time de-lay with caching,should be further optimized while the objective function and the constraints are non-convex.Therefore,we first jointly optimize commu-nication resources B,computing resources F and of-floading rates X with alternating iteration and convex optimization method,and then determine the value of caching decision Y with branch-and-bound(BB)al-gorithm.Numerical results show that UAV assisting partial task offloading with content caching is supe-rior to local computing and full offloading mechanism without caching,and meanwhile the cost of whole sys-tem gets further optimized with our proposed scheme.展开更多
The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive require...The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive requirements,especially in some infrastructure-limited areas or some emergency scenarios.However,the multi-UAVassisted MEC network remains largely unexplored.In this paper,the dynamic trajectory optimization and computation offloading are studied in a multi-UAVassisted MEC system where multiple UAVs fly over a target area with different trajectories to serve ground users.By considering the dynamic channel condition and random task arrival and jointly optimizing UAVs'trajectories,user association,and subchannel assignment,the average long-term sum of the user energy consumption minimization problem is formulated.To address the problem involving both discrete and continuous variables,a hybrid decision deep reinforcement learning(DRL)-based intelligent energyefficient resource allocation and trajectory optimization algorithm is proposed,named HDRT algorithm,where deep Q network(DQN)and deep deterministic policy gradient(DDPG)are invoked to process discrete and continuous variables,respectively.Simulation results show that the proposed HDRT algorithm converges fast and outperforms other benchmarks in the aspect of user energy consumption and latency.展开更多
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
近年来,健康医疗大数据产业被列入国家大数据战略布局,带来了健康医疗模式的深刻变化,但医疗行业整体数据安全和个人信息保护也面临着更大的威胁。本研究通过对传统远程医疗行业痛点的分析,提出了一种基于5G移动边缘计算的远程云诊疗平...近年来,健康医疗大数据产业被列入国家大数据战略布局,带来了健康医疗模式的深刻变化,但医疗行业整体数据安全和个人信息保护也面临着更大的威胁。本研究通过对传统远程医疗行业痛点的分析,提出了一种基于5G移动边缘计算的远程云诊疗平台设计。该设计采用5G端到端网络切片结合多重服务质量(Quality of Service,QoS)技术,实现了业务差异化服务水平协议(Service Level Agreement,SLA)保障,构筑了端到端安全、可靠,以及带宽和时延满足业务要求的虚拟专网,持续提高了智慧医疗行业的便捷化、自动化和智能化,从多方面保障了人们健康,显著提升了医疗服务水平。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
基金supported by National Natural Science Foundation of China(No.61821001)Science and Technology Key Project of Guangdong Province,China(2019B010157001).
文摘In this paper,unmanned aerial vehicle(UAV)is adopted to serve as aerial base station(ABS)and mobile edge computing(MEC)platform for wire-less communication systems.When Internet of Things devices(IoTDs)cannot cope with computation-intensive and/or time-sensitive tasks,part of tasks is offloaded to the UAV side,and UAV process them with its own computing resources and caching resources.Thus,the burden of IoTDs gets relieved under the satisfaction of the quality of service(QoS)require-ments.However,owing to the limited resources of UAV,the cost of whole system,i.e.,that is defined as the weighted sum of energy consumption and time de-lay with caching,should be further optimized while the objective function and the constraints are non-convex.Therefore,we first jointly optimize commu-nication resources B,computing resources F and of-floading rates X with alternating iteration and convex optimization method,and then determine the value of caching decision Y with branch-and-bound(BB)al-gorithm.Numerical results show that UAV assisting partial task offloading with content caching is supe-rior to local computing and full offloading mechanism without caching,and meanwhile the cost of whole sys-tem gets further optimized with our proposed scheme.
基金supported by National Natural Science Foundation of China(No.62471254)National Natural Science Foundation of China(No.92367302)。
文摘The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive requirements,especially in some infrastructure-limited areas or some emergency scenarios.However,the multi-UAVassisted MEC network remains largely unexplored.In this paper,the dynamic trajectory optimization and computation offloading are studied in a multi-UAVassisted MEC system where multiple UAVs fly over a target area with different trajectories to serve ground users.By considering the dynamic channel condition and random task arrival and jointly optimizing UAVs'trajectories,user association,and subchannel assignment,the average long-term sum of the user energy consumption minimization problem is formulated.To address the problem involving both discrete and continuous variables,a hybrid decision deep reinforcement learning(DRL)-based intelligent energyefficient resource allocation and trajectory optimization algorithm is proposed,named HDRT algorithm,where deep Q network(DQN)and deep deterministic policy gradient(DDPG)are invoked to process discrete and continuous variables,respectively.Simulation results show that the proposed HDRT algorithm converges fast and outperforms other benchmarks in the aspect of user energy consumption and latency.
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.
文摘近年来,健康医疗大数据产业被列入国家大数据战略布局,带来了健康医疗模式的深刻变化,但医疗行业整体数据安全和个人信息保护也面临着更大的威胁。本研究通过对传统远程医疗行业痛点的分析,提出了一种基于5G移动边缘计算的远程云诊疗平台设计。该设计采用5G端到端网络切片结合多重服务质量(Quality of Service,QoS)技术,实现了业务差异化服务水平协议(Service Level Agreement,SLA)保障,构筑了端到端安全、可靠,以及带宽和时延满足业务要求的虚拟专网,持续提高了智慧医疗行业的便捷化、自动化和智能化,从多方面保障了人们健康,显著提升了医疗服务水平。
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.
