Benefited from wireless power transfer(WPT)and mobile-edge computing(MEC),wireless powered MEC systems have attracted widespread attention.Specifically,we design an online offloading scheme based on deep reinforcement...Benefited from wireless power transfer(WPT)and mobile-edge computing(MEC),wireless powered MEC systems have attracted widespread attention.Specifically,we design an online offloading scheme based on deep reinforcement learning that maximizes the computation rate and minimizes the energy consumption of all wireless devices(WDs).Extensive results validate that the proposed scheme can achieve better tradeoff between energy consumption and computation delay.展开更多
Under the situations of energy dilemma, energy Internet has become one of the most important technologies in international academic and industrial areas. However, massive small data from users, which are too scattered...Under the situations of energy dilemma, energy Internet has become one of the most important technologies in international academic and industrial areas. However, massive small data from users, which are too scattered and unsuitable for compression, can easily exhaust computational resources and lower random access possibility, thereby reducing system performance. Moreover, electric substations are sensitive to transmission latency of user data, such as controlling information. However, the traditional energy Internet usually could not meet requirements. Integrating mobile-edge computing makes energy Internet convenient for data acquisition,processing, management, and accessing. In this paper, we propose a novel framework for energy Internet to improve random access possibility and reduce transmission latency. This framework utilizes the local area network to collect data from users and makes conducting data compression for energy Internet possible. Simulation results show that this architecture can enhance random access possibility by a large margin and reduce transmission latency without extra energy consumption overhead.展开更多
Mobile-edge computing(MEC),enabling to offload computing tasks on mobile devices towards edge servers,can reduce the terminals cost.However,a single MEC sever usually has limited computing capabilities,which can not m...Mobile-edge computing(MEC),enabling to offload computing tasks on mobile devices towards edge servers,can reduce the terminals cost.However,a single MEC sever usually has limited computing capabilities,which can not meet a large number of terminals’requirements.In this paper,we consider an ultra-dense networks(UDN)scenario where the macro base stations(MBSs)are assisted by MEC severs.In particular,we first construct system model for MEC assisted UDN,and build the system overhead minimization.Next,in order to solve the problem,we transform the problem into three sub-problems,i.e.,offloading strategies subproblem,channel assignments subproblem,and power allocation subproblem.Then,employing joint offloading and resource allocation algorithms,we obtain the optimal joint strategy for the MEC assisted UDNs.Finally,simulations are conducted to evaluate the performance of our proposed algorithms.Numerical results show that obtained algorithms can effectively reduce the energy consumption of the system and improve the overall performance of the system.展开更多
The growing development of the Internet of Things(IoT)is accelerating the emergence and growth of new IoT services and applications,which will result in massive amounts of data being generated,transmitted and pro-cess...The growing development of the Internet of Things(IoT)is accelerating the emergence and growth of new IoT services and applications,which will result in massive amounts of data being generated,transmitted and pro-cessed in wireless communication networks.Mobile Edge Computing(MEC)is a desired paradigm to timely process the data from IoT for value maximization.In MEC,a number of computing-capable devices are deployed at the network edge near data sources to support edge computing,such that the long network transmission delay in cloud computing paradigm could be avoided.Since an edge device might not always have sufficient resources to process the massive amount of data,computation offloading is significantly important considering the coop-eration among edge devices.However,the dynamic traffic characteristics and heterogeneous computing capa-bilities of edge devices challenge the offloading.In addition,different scheduling schemes might provide different computation delays to the offloaded tasks.Thus,offloading in mobile nodes and scheduling in the MEC server are coupled to determine service delay.This paper seeks to guarantee low delay for computation intensive applica-tions by jointly optimizing the offloading and scheduling in such an MEC system.We propose a Delay-Greedy Computation Offloading(DGCO)algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices.A Reinforcement Learning-based Parallel Scheduling(RLPS)algorithm is further designed to schedule offloaded tasks in the multi-core MEC server.With an offloading delay broadcast mechanism,the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio maximization.Finally,the simulation results show that our proposal can bound the end-to-end delay of various tasks.Even under slightly heavy task load,the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95%,while that given by benchmarked algorithms is reduced to intolerable value.The simulation results are demonstrated the effective-ness of DGCO-RLPS for delay guarantee in MEC.展开更多
There is a growing awareness among industry players of reaping the benefits of mobile-cloud convergence by extending today's unmodified cloud to a decentralized two-level cloud-cloudlet architecture based on emerg...There is a growing awareness among industry players of reaping the benefits of mobile-cloud convergence by extending today's unmodified cloud to a decentralized two-level cloud-cloudlet architecture based on emerging mobile-edge computing(MEC) capabilities. In light of future 5G mobile networks moving toward decentralization based on cloudlets, intelligent base stations, and MEC, the inherent distributed processing and storage capabilities of radio-and-fiber(R&F) networks may be exploited for new applications, e.g., cognitive assistance, augmented reality, or cloud robotics. In this paper, we first revisit fiber-wireless(Fi Wi) networks in the context of conventional clouds and emerging cloudlets, thereby highlighting the limitations of conventional radio-overfiber(Ro F) networks such as China Mobile's centralized cloud radio access network(C-RAN) to meet the aforementioned trends. Furthermore, we pay close attention to the specific design challenges of data center networks and revisit our switchless arrayedwaveguide grating(AWG) based network with efficient support of east-west flows and enhanced scalability.展开更多
Mobile-Edge Computing(MEC)displaces cloud services as closely as possible to the end user.This enables the edge servers to execute the offloaded tasks that are requested by the users,which in turn decreases the energy...Mobile-Edge Computing(MEC)displaces cloud services as closely as possible to the end user.This enables the edge servers to execute the offloaded tasks that are requested by the users,which in turn decreases the energy consumption and the turnaround time delay.However,as a result of a hostile environment or in catastrophic zones with no network,it could be difficult to deploy such edge servers.Unmanned Aerial Vehicles(UAVs)can be employed in such scenarios.The edge servers mounted on these UAVs assist with task offloading.For the majority of IoT applications,the execution times of tasks are often crucial.Therefore,UAVs tend to have a limited energy supply.This study presents an approach to offload IoT user applications based on the usage of Voronoi diagrams to determine task delays and cluster IoT devices dynamically as a first step.Second,the UAV flies over each cluster to perform the offloading process.In addition,we propose a Graphics Processing Unit(GPU)-based parallelization of particle swarm optimization to balance the cluster sizes and identify the shortest path along these clusters while minimizing the UAV flying time and energy consumption.Some evaluation results are given to demonstrate the effectiveness of the presented offloading strategy.展开更多
With the development of the Industrial Internet of Things(IIoT),end devices(EDs)are equipped with more functions to capture information.Therefore,a large amount of data is generated at the edge of the network and need...With the development of the Industrial Internet of Things(IIoT),end devices(EDs)are equipped with more functions to capture information.Therefore,a large amount of data is generated at the edge of the network and needs to be processed.However,no matter whether these computing tasks are offloaded to traditional central clusters or mobile edge computing(MEC)devices,the data is short of security and may be changed during transmission.In view of this challenge,this paper proposes a trusted task offloading optimization scheme that can offer low latency and high bandwidth services for IIoT with data security.Blockchain technology is adopted to ensure data consistency.Meanwhile,to reduce the impact of low throughput of blockchain on task offloading performance,we design the processes of consensus and offloading as a Markov decision process(MDP)by defining states,actions,and rewards.Deep reinforcement learning(DRL)algorithm is introduced to dynamically select offloading actions.To accelerate the optimization,we design a novel reward function for the DRL algorithm according to the scale and computational complexity of the task.Experiments demonstrate that compared with methods without optimization,our mechanism performs better when it comes to the number of task offloading and throughput of blockchain.展开更多
Integrating the blockchain technology into mobile-edge computing(MEC)networks with multiple cooperative MEC servers(MECS)providing a promising solution to improving resource utilization,and helping establish a secure ...Integrating the blockchain technology into mobile-edge computing(MEC)networks with multiple cooperative MEC servers(MECS)providing a promising solution to improving resource utilization,and helping establish a secure reward mechanism that can facilitate load balancing among MECS.In addition,intelligent management of service caching and load balancing can improve the network utility in MEC blockchain networks with multiple types of workloads.In this paper,we investigate a learningbased joint service caching and load balancing policy for optimizing the communication and computation resources allocation,so as to improve the resource utilization of MEC blockchain networks.We formulate the problem as a challenging long-term network revenue maximization Markov decision process(MDP)problem.To address the highly dynamic and high dimension of system states,we design a joint service caching and load balancing algorithm based on the double-dueling Deep Q network(DQN)approach.The simulation results validate the feasibility and superior performance of our proposed algorithm over several baseline schemes.展开更多
Mobile-edge computing(MEC)is a promising technology for the fifth-generation(5G)and sixth-generation(6G)architectures,which provides resourceful computing capabilities for Internet of Things(IoT)devices,such as virtua...Mobile-edge computing(MEC)is a promising technology for the fifth-generation(5G)and sixth-generation(6G)architectures,which provides resourceful computing capabilities for Internet of Things(IoT)devices,such as virtual reality,mobile devices,and smart cities.In general,these IoT applications always bring higher energy consumption than traditional applications,which are usually energy-constrained.To provide persistent energy,many references have studied the offloading problem to save energy consumption.However,the dynamic environment dramatically increases the optimization difficulty of the offloading decision.In this paper,we aim to minimize the energy consumption of the entireMECsystemunder the latency constraint by fully considering the dynamic environment.UnderMarkov games,we propose amulti-agent deep reinforcement learning approach based on the bi-level actorcritic learning structure to jointly optimize the offloading decision and resource allocation,which can solve the combinatorial optimization problem using an asymmetric method and compute the Stackelberg equilibrium as a better convergence point than Nash equilibrium in terms of Pareto superiority.Our method can better adapt to a dynamic environment during the data transmission than the single-agent strategy and can effectively tackle the coordination problem in the multi-agent environment.The simulation results show that the proposed method could decrease the total computational overhead by 17.8%compared to the actor-critic-based method and reduce the total computational overhead by 31.3%,36.5%,and 44.7%compared with randomoffloading,all local execution,and all offloading execution,respectively.展开更多
Physical sensors,intelligent sensors,and output recommenda-tions are all examples of smart health technology that can be used to monitor patients’health and change their behavior.Smart health is an Internet-of-Things...Physical sensors,intelligent sensors,and output recommenda-tions are all examples of smart health technology that can be used to monitor patients’health and change their behavior.Smart health is an Internet-of-Things(IoT)-aware network and sensing infrastructure that provides real-time,intelligent,and ubiquitous healthcare services.Because of the rapid development of cloud computing,as well as related technologies such as fog computing,smart health research is progressively moving in the right direction.Cloud,fog computing,IoT sensors,blockchain,privacy and security,and other related technologies have been the focus of smart health research in recent years.At the moment,the focus in cloud and smart health research is on how to use the cloud to solve the problem of enormous health data and enhance service performance,including cloud storage,retrieval,and calculation of health big data.This article reviews state-of-the-art edge computing methods that has shifted to the collection,transmission,and calculation of health data,which includes various sensors and wearable devices used to collect health data,various wireless sensor technologies,and how to process health data and improve edge performance,among other things.Finally,the typical smart health application cases,blockchain’s application in smart health,and related privacy and security issues were reviewed,as well as future difficulties and potential for smart health services.The comparative analysis provides a reference for the the mobile edge computing in healthcare systems.展开更多
This paper investigates the multi-Unmanned Aerial Vehicle(UAV)-assisted wireless-powered Mobile Edge Computing(MEC)system,where UAVs provide computation and powering services to mobile terminals.We aim to maximize the...This paper investigates the multi-Unmanned Aerial Vehicle(UAV)-assisted wireless-powered Mobile Edge Computing(MEC)system,where UAVs provide computation and powering services to mobile terminals.We aim to maximize the number of completed computation tasks by jointly optimizing the offloading decisions of all terminals and the trajectory planning of all UAVs.The action space of the system is extremely large and grows exponentially with the number of UAVs.In this case,single-agent learning will require an overlarge neural network,resulting in insufficient exploration.However,the offloading decisions and trajectory planning are two subproblems performed by different executants,providing an opportunity for problem-solving.We thus adopt the idea of decomposition and propose a 2-Tiered Multi-agent Soft Actor-Critic(2T-MSAC)algorithm,decomposing a single neural network into multiple small-scale networks.In the first tier,a single agent is used for offloading decisions,and an online pretrained model based on imitation learning is specially designed to accelerate the training process of this agent.In the second tier,UAVs utilize multiple agents to plan their trajectories.Each agent exerts its influence on the parameter update of other agents through actions and rewards,thereby achieving joint optimization.Simulation results demonstrate that the proposed algorithm can be applied to scenarios with various location distributions of terminals,outperforming existing benchmarks that perform well only in specific scenarios.In particular,2T-MSAC increases the number of completed tasks by 45.5%in the scenario with uneven terminal distributions.Moreover,the pretrained model based on imitation learning reduces the convergence time of 2T-MSAC by 58.2%.展开更多
基金National Natural Science Foundation of China(No.61902060)Fundamental Research Fund for the Central Universities,China(No.2232019D3-51)Shanghai Sailing Program,China(No.19YF1402100).
文摘Benefited from wireless power transfer(WPT)and mobile-edge computing(MEC),wireless powered MEC systems have attracted widespread attention.Specifically,we design an online offloading scheme based on deep reinforcement learning that maximizes the computation rate and minimizes the energy consumption of all wireless devices(WDs).Extensive results validate that the proposed scheme can achieve better tradeoff between energy consumption and computation delay.
基金supported by the Beijing Municipal Science and Technology Commission Research (No. Z171100005217001)the National Science and Technology Major Project (No. 2018ZX03001016)+4 种基金the Fundamental Research Funds for the Central Universities (No. 2018RC06)the National Key R&D Program of China (No. 2017YFC0112802)the Beijing Laboratory of Advanced Information Networksthe Beijing Key Laboratory of Network System Architecture and Convergencethe 111 project B17007
文摘Under the situations of energy dilemma, energy Internet has become one of the most important technologies in international academic and industrial areas. However, massive small data from users, which are too scattered and unsuitable for compression, can easily exhaust computational resources and lower random access possibility, thereby reducing system performance. Moreover, electric substations are sensitive to transmission latency of user data, such as controlling information. However, the traditional energy Internet usually could not meet requirements. Integrating mobile-edge computing makes energy Internet convenient for data acquisition,processing, management, and accessing. In this paper, we propose a novel framework for energy Internet to improve random access possibility and reduce transmission latency. This framework utilizes the local area network to collect data from users and makes conducting data compression for energy Internet possible. Simulation results show that this architecture can enhance random access possibility by a large margin and reduce transmission latency without extra energy consumption overhead.
基金the Natural Science Foundation of Henan(202300410292)the Key Scientific Projects of Henan Higher Education Institutions(19A510018)+5 种基金the Key Scientific Projects of Henan Higher Education Institutions(20A510008)the Key Scientific Projects of Henan Higher Education Institutions(21A510008)the Key Scientific and Technological Projects(202102210120)the Key Scientific and Technological Projects(212102210553)the Foundation for Young Backbone Teachers in Higher Education Institutions(2018GGJS126)Henan key Laboratory for Big Data Processing and Analytics of Electronic Commerce(2020-KF-6)。
文摘Mobile-edge computing(MEC),enabling to offload computing tasks on mobile devices towards edge servers,can reduce the terminals cost.However,a single MEC sever usually has limited computing capabilities,which can not meet a large number of terminals’requirements.In this paper,we consider an ultra-dense networks(UDN)scenario where the macro base stations(MBSs)are assisted by MEC severs.In particular,we first construct system model for MEC assisted UDN,and build the system overhead minimization.Next,in order to solve the problem,we transform the problem into three sub-problems,i.e.,offloading strategies subproblem,channel assignments subproblem,and power allocation subproblem.Then,employing joint offloading and resource allocation algorithms,we obtain the optimal joint strategy for the MEC assisted UDNs.Finally,simulations are conducted to evaluate the performance of our proposed algorithms.Numerical results show that obtained algorithms can effectively reduce the energy consumption of the system and improve the overall performance of the system.
基金supported in part by the National Natural Science Foundation of China under Grant 61901128,62273109the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(21KJB510032).
文摘The growing development of the Internet of Things(IoT)is accelerating the emergence and growth of new IoT services and applications,which will result in massive amounts of data being generated,transmitted and pro-cessed in wireless communication networks.Mobile Edge Computing(MEC)is a desired paradigm to timely process the data from IoT for value maximization.In MEC,a number of computing-capable devices are deployed at the network edge near data sources to support edge computing,such that the long network transmission delay in cloud computing paradigm could be avoided.Since an edge device might not always have sufficient resources to process the massive amount of data,computation offloading is significantly important considering the coop-eration among edge devices.However,the dynamic traffic characteristics and heterogeneous computing capa-bilities of edge devices challenge the offloading.In addition,different scheduling schemes might provide different computation delays to the offloaded tasks.Thus,offloading in mobile nodes and scheduling in the MEC server are coupled to determine service delay.This paper seeks to guarantee low delay for computation intensive applica-tions by jointly optimizing the offloading and scheduling in such an MEC system.We propose a Delay-Greedy Computation Offloading(DGCO)algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices.A Reinforcement Learning-based Parallel Scheduling(RLPS)algorithm is further designed to schedule offloaded tasks in the multi-core MEC server.With an offloading delay broadcast mechanism,the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio maximization.Finally,the simulation results show that our proposal can bound the end-to-end delay of various tasks.Even under slightly heavy task load,the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95%,while that given by benchmarked algorithms is reduced to intolerable value.The simulation results are demonstrated the effective-ness of DGCO-RLPS for delay guarantee in MEC.
文摘There is a growing awareness among industry players of reaping the benefits of mobile-cloud convergence by extending today's unmodified cloud to a decentralized two-level cloud-cloudlet architecture based on emerging mobile-edge computing(MEC) capabilities. In light of future 5G mobile networks moving toward decentralization based on cloudlets, intelligent base stations, and MEC, the inherent distributed processing and storage capabilities of radio-and-fiber(R&F) networks may be exploited for new applications, e.g., cognitive assistance, augmented reality, or cloud robotics. In this paper, we first revisit fiber-wireless(Fi Wi) networks in the context of conventional clouds and emerging cloudlets, thereby highlighting the limitations of conventional radio-overfiber(Ro F) networks such as China Mobile's centralized cloud radio access network(C-RAN) to meet the aforementioned trends. Furthermore, we pay close attention to the specific design challenges of data center networks and revisit our switchless arrayedwaveguide grating(AWG) based network with efficient support of east-west flows and enhanced scalability.
基金funded by the University of Jeddah,Saudi Arabia,under Grant No.(UJ-20-102-DR).
文摘Mobile-Edge Computing(MEC)displaces cloud services as closely as possible to the end user.This enables the edge servers to execute the offloaded tasks that are requested by the users,which in turn decreases the energy consumption and the turnaround time delay.However,as a result of a hostile environment or in catastrophic zones with no network,it could be difficult to deploy such edge servers.Unmanned Aerial Vehicles(UAVs)can be employed in such scenarios.The edge servers mounted on these UAVs assist with task offloading.For the majority of IoT applications,the execution times of tasks are often crucial.Therefore,UAVs tend to have a limited energy supply.This study presents an approach to offload IoT user applications based on the usage of Voronoi diagrams to determine task delays and cluster IoT devices dynamically as a first step.Second,the UAV flies over each cluster to perform the offloading process.In addition,we propose a Graphics Processing Unit(GPU)-based parallelization of particle swarm optimization to balance the cluster sizes and identify the shortest path along these clusters while minimizing the UAV flying time and energy consumption.Some evaluation results are given to demonstrate the effectiveness of the presented offloading strategy.
基金supported by the Projects of Software of Big Data Processing Tool(TC210804V-1)Big Data Risk Screening Model Procurement(No.S20200).
文摘With the development of the Industrial Internet of Things(IIoT),end devices(EDs)are equipped with more functions to capture information.Therefore,a large amount of data is generated at the edge of the network and needs to be processed.However,no matter whether these computing tasks are offloaded to traditional central clusters or mobile edge computing(MEC)devices,the data is short of security and may be changed during transmission.In view of this challenge,this paper proposes a trusted task offloading optimization scheme that can offer low latency and high bandwidth services for IIoT with data security.Blockchain technology is adopted to ensure data consistency.Meanwhile,to reduce the impact of low throughput of blockchain on task offloading performance,we design the processes of consensus and offloading as a Markov decision process(MDP)by defining states,actions,and rewards.Deep reinforcement learning(DRL)algorithm is introduced to dynamically select offloading actions.To accelerate the optimization,we design a novel reward function for the DRL algorithm according to the scale and computational complexity of the task.Experiments demonstrate that compared with methods without optimization,our mechanism performs better when it comes to the number of task offloading and throughput of blockchain.
基金supported in part by the National Natural Science Foundation of China 62072096the Fundamental Research Funds for the Central Universities under Grant 2232020A-12+4 种基金the International S&T Cooperation Program of Shanghai Science and Technology Commission under Grant 20220713000the Young Top-notch Talent Program in Shanghaithe"Shuguang Program"of Shanghai Education Development Foundation and Shanghai Municipal Education Commissionthe Fundamental Research Funds for the Central Universities and Graduate Student Innovation Fund of Donghua University CUSF-DH-D-2019093supported in part by the NSF under grants CNS-2107190 and ECCS-1923717。
文摘Integrating the blockchain technology into mobile-edge computing(MEC)networks with multiple cooperative MEC servers(MECS)providing a promising solution to improving resource utilization,and helping establish a secure reward mechanism that can facilitate load balancing among MECS.In addition,intelligent management of service caching and load balancing can improve the network utility in MEC blockchain networks with multiple types of workloads.In this paper,we investigate a learningbased joint service caching and load balancing policy for optimizing the communication and computation resources allocation,so as to improve the resource utilization of MEC blockchain networks.We formulate the problem as a challenging long-term network revenue maximization Markov decision process(MDP)problem.To address the highly dynamic and high dimension of system states,we design a joint service caching and load balancing algorithm based on the double-dueling Deep Q network(DQN)approach.The simulation results validate the feasibility and superior performance of our proposed algorithm over several baseline schemes.
基金supported by the National Natural Science Foundation of China(62162050)the Fundamental Research Funds for the Central Universities(No.N2217002)the Natural Science Foundation of Liaoning ProvincialDepartment of Science and Technology(No.2022-KF-11-04).
文摘Mobile-edge computing(MEC)is a promising technology for the fifth-generation(5G)and sixth-generation(6G)architectures,which provides resourceful computing capabilities for Internet of Things(IoT)devices,such as virtual reality,mobile devices,and smart cities.In general,these IoT applications always bring higher energy consumption than traditional applications,which are usually energy-constrained.To provide persistent energy,many references have studied the offloading problem to save energy consumption.However,the dynamic environment dramatically increases the optimization difficulty of the offloading decision.In this paper,we aim to minimize the energy consumption of the entireMECsystemunder the latency constraint by fully considering the dynamic environment.UnderMarkov games,we propose amulti-agent deep reinforcement learning approach based on the bi-level actorcritic learning structure to jointly optimize the offloading decision and resource allocation,which can solve the combinatorial optimization problem using an asymmetric method and compute the Stackelberg equilibrium as a better convergence point than Nash equilibrium in terms of Pareto superiority.Our method can better adapt to a dynamic environment during the data transmission than the single-agent strategy and can effectively tackle the coordination problem in the multi-agent environment.The simulation results show that the proposed method could decrease the total computational overhead by 17.8%compared to the actor-critic-based method and reduce the total computational overhead by 31.3%,36.5%,and 44.7%compared with randomoffloading,all local execution,and all offloading execution,respectively.
基金supported by the Ministry of Education,Malaysia(Grant Code:FRGS/1/2018/ICT02/UKM/02/6).
文摘Physical sensors,intelligent sensors,and output recommenda-tions are all examples of smart health technology that can be used to monitor patients’health and change their behavior.Smart health is an Internet-of-Things(IoT)-aware network and sensing infrastructure that provides real-time,intelligent,and ubiquitous healthcare services.Because of the rapid development of cloud computing,as well as related technologies such as fog computing,smart health research is progressively moving in the right direction.Cloud,fog computing,IoT sensors,blockchain,privacy and security,and other related technologies have been the focus of smart health research in recent years.At the moment,the focus in cloud and smart health research is on how to use the cloud to solve the problem of enormous health data and enhance service performance,including cloud storage,retrieval,and calculation of health big data.This article reviews state-of-the-art edge computing methods that has shifted to the collection,transmission,and calculation of health data,which includes various sensors and wearable devices used to collect health data,various wireless sensor technologies,and how to process health data and improve edge performance,among other things.Finally,the typical smart health application cases,blockchain’s application in smart health,and related privacy and security issues were reviewed,as well as future difficulties and potential for smart health services.The comparative analysis provides a reference for the the mobile edge computing in healthcare systems.
基金supported in part by the National Natural Science Foundation of China under Grant 62271306,Grant 62072410,and Grant 62331017in part by the Fundamental Research Funds for the Provincial Universities of Zhejiang under Grant RF-B2022002。
文摘This paper investigates the multi-Unmanned Aerial Vehicle(UAV)-assisted wireless-powered Mobile Edge Computing(MEC)system,where UAVs provide computation and powering services to mobile terminals.We aim to maximize the number of completed computation tasks by jointly optimizing the offloading decisions of all terminals and the trajectory planning of all UAVs.The action space of the system is extremely large and grows exponentially with the number of UAVs.In this case,single-agent learning will require an overlarge neural network,resulting in insufficient exploration.However,the offloading decisions and trajectory planning are two subproblems performed by different executants,providing an opportunity for problem-solving.We thus adopt the idea of decomposition and propose a 2-Tiered Multi-agent Soft Actor-Critic(2T-MSAC)algorithm,decomposing a single neural network into multiple small-scale networks.In the first tier,a single agent is used for offloading decisions,and an online pretrained model based on imitation learning is specially designed to accelerate the training process of this agent.In the second tier,UAVs utilize multiple agents to plan their trajectories.Each agent exerts its influence on the parameter update of other agents through actions and rewards,thereby achieving joint optimization.Simulation results demonstrate that the proposed algorithm can be applied to scenarios with various location distributions of terminals,outperforming existing benchmarks that perform well only in specific scenarios.In particular,2T-MSAC increases the number of completed tasks by 45.5%in the scenario with uneven terminal distributions.Moreover,the pretrained model based on imitation learning reduces the convergence time of 2T-MSAC by 58.2%.
