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A Deep Reinforcement Learning-Based Pre-Allocation Mechanism for Efficient Task Offloading in Mobile Edge Computing 认领 引用
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作者 Chaobin Wang Xianghong Tang +2 位作者 Jianguang Lu Jing Yang Panliang Yuan 《Computers, Materials & Continua》 SCIE EI 2026年第7期1086-1113,共28页
Mobile Edge Computing(MEC)facilitates the rapid response and energy-efficient execution of tasks on mobile devices.However,determining whether and where to offload tasks remains a significant challenge due to the cons... Mobile Edge Computing(MEC)facilitates the rapid response and energy-efficient execution of tasks on mobile devices.However,determining whether and where to offload tasks remains a significant challenge due to the constantly changing character of workloads in MEC environments.To address this issue,this paper proposes PreAlloc-A2C—a deep reinforcement learning actor-critic-based framework that calculates allocation scores by leveraging both task features(task size,required completion time,and waiting time)and server features(queue length and historical workload).This design enables fully distributed task offloading decisions without centralized coordination.Additionally,a Long Short-Term Memory(LSTM)network is integrated to forecast impending server loads,thereby supporting adaptive scheduling.A tailored reward function is also designed to jointly optimize three key performance metrics:task delay,device energy consumption,and task drop rate.Extensive experiments are conducted to evaluate PreAlloc-A2C against five baseline algorithms:Particle Swarm Optimization(PSO),Advantage Actor-Critic(A2C),Deep Q-Network(DQN),Double Deep Q-Network(DDQN),and Dueling Deep Q-Network(Dueling DQN).The results show that PreAlloc-A2C outperforms all baselines,achieving lower latency,reduced energy consumption,and a lower task drop rate. 展开更多
关键词 Mobile edge computing task offloading deep reinforcement learning
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Predictive-Q Learning Based Interference-and-Mobility Aware Dual-Path Routing for UAV Swarm Networks with Mobile Edge Computing 认领 引用
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作者 Zhihao Dong Huakui Sun +1 位作者 Yueyue Tao Daosen Zhai 《Computers, Materials & Continua》 SCIE EI 2026年第9期1774-1791,共18页
High-mobility Unmanned Aerial Vehicle(UAV)swarm networks suffer from fast-varying connectivity and interference,and therefore routing decisions must jointly account for link instability and topology changes.By leverag... High-mobility Unmanned Aerial Vehicle(UAV)swarm networks suffer from fast-varying connectivity and interference,and therefore routing decisions must jointly account for link instability and topology changes.By leveraging mobile edge computing(MEC)capabilities,each UAV can perform online routing decisions locally without relying on centralized controllers.This paper develops a Predictive-Q learning framework for dynamic routing under interference and mobility,where the Q-value is trained by a multi-factor reward that explicitly models retransmission costs,predicts link lifetime from relative motion,and anticipates forward connectivity and neighbor redundancy.To further enhance reliability under harsh interference,we extend Predictive-Q with a lightweight dual-path forwarding mechanism.Specifically,it conditionally splits a backup routing when the primary next hop becomes unreliable,and terminates the backup early when continued forwarding is unlikely to be beneficial,thereby controlling overhead.Simulation results demonstrate that,compared with existing methods,the proposed Predictive-Q-Dual improves packet delivery ratio by more than 15%over GPSR under strong interference while maintaining low delay and energy consumption across varying interference intensity,node density,and mobility speed. 展开更多
关键词 UAV swarm networks routing reinforcement learning dual-path forwarding mobile edge computing
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Task Offloading and Edge Computing in IoT-Gaps, Challenges and Future Directions 认领 引用 被引量：1
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作者 Hitesh Mohapatra 《Computers, Materials & Continua》 SCIE EI 2026年第6期268-296,共29页
This review examines current approaches to real-time decision-making and task optimization in Internet of Things systems through the application of machine learning models deployed at the network edge.Existing literat... This review examines current approaches to real-time decision-making and task optimization in Internet of Things systems through the application of machine learning models deployed at the network edge.Existing literature shows that edge-based distributed intelligence reduces cloud dependency.It addresses transmission latency,device energy use,and bandwidth limits.Recent optimization strategies employ dynamic task offloading mechanisms to determine optimal workload placement across local devices and edge servers without centralized coordination.Empirical findings from the literature indicate performance improvements with latency reductions of approximately 32.8%and energy efficiency gains of 27.4%compared to conventional cloud-centric models.However,critical gaps remain in current methodologies.Most studies focus on static network topologies and do not adequately address load balancing across multiple edge nodes.Security vulnerabilities during task transmission are underexplored,and privacy considerations for sensitive data remain insufficiently integrated into existing frameworks.Task caching strategies and fault tolerance mechanisms require further investigation in highly dynamic environments.The ability of existing approaches to handle large-scale deployments and complex edge-cloud collaborative scenarios has not been thoroughly validated.This review synthesizes current progress while identifying fundamental challenges that must be resolved for practical deployment in time-sensitive applications spanning smart manufacturing,autonomous systems,and healthcare monitoring.Future work should prioritize robust security integration,efficient load distribution,and scalability across heterogeneous edge infrastructures. 展开更多
关键词 Edge computing task offloading deep reinforcement learning mobile edge computing IoT performance optimization methodological rigor assessment
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Intelligent Energy-Efficient Resource Allocation for Multi-UAV-Assisted Mobile Edge Computing Networks 认领 引用 被引量：1
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作者 Hu Han Shen Le +2 位作者 Zhou Fuhui Wang Qun Zhu Hongbo 《China Communications》 SCIE EI CSCD 2025年第4期339-355,共17页
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
关键词 dynamic trajectory optimization intelligent resource allocation unmanned aerial vehicle uav assisted uav assisted mec energy efficiency smart applications mobile edge computing mec deep reinforcement learning
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Computational Offloading and Resource Allocation for Internet of Vehicles Based on UAV-Assisted Mobile Edge Computing System 认领 引用
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作者 Fang Yujie Li Meng +3 位作者 Si Pengbo Yang Ruizhe Sun Enchang Zhang Yanhua 《China Communications》 SCIE EI CSCD 2025年第9期333-351,共19页
As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational ... As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational capability of the vehicle which reduces task processing latency and power con-sumption effectively and meets the quality of service requirements of vehicle users.However,there are still some problems in the MEC-assisted IoV system such as poor connectivity and high cost.Unmanned aerial vehicles(UAVs)equipped with MEC servers have become a promising approach for providing com-munication and computing services to mobile vehi-cles.Hence,in this article,an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is presented.Through joint consideration of computational offloading decisions and computational resource allocation,the optimiza-tion problem of our proposed architecture is presented to reduce system energy consumption and delay.For purpose of tackling this issue,the original non-convex issue is converted into a convex issue and the alternat-ing direction method of multipliers-based distributed optimal scheme is developed.The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes,and the convergence of the proposed scheme is also significant. 展开更多
关键词 computational offloading Internet of Vehicles mobile edge computing resource optimization unmanned aerial vehicle
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Mobile Edge Communications, Computing, and Caching(MEC3) Technology in the Maritime Communication Network 认领 引用 被引量：22
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作者 Jie Zeng Jiaying Sun +1 位作者 Binwei Wu Xin Su 《China Communications》 SCIE CSCD 2020年第5期223-234,共12页
With the increasing maritime activities and the rapidly developing maritime economy, the fifth-generation(5G) mobile communication system is expected to be deployed at the ocean. New technologies need to be explored t... With the increasing maritime activities and the rapidly developing maritime economy, the fifth-generation(5G) mobile communication system is expected to be deployed at the ocean. New technologies need to be explored to meet the requirements of ultra-reliable and low latency communications(URLLC) in the maritime communication network(MCN). Mobile edge computing(MEC) can achieve high energy efficiency in MCN at the cost of suffering from high control plane latency and low reliability. In terms of this issue, the mobile edge communications, computing, and caching(MEC3) technology is proposed to sink mobile computing, network control, and storage to the edge of the network. New methods that enable resource-efficient configurations and reduce redundant data transmissions can enable the reliable implementation of computing-intension and latency-sensitive applications. The key technologies of MEC3 to enable URLLC are analyzed and optimized in MCN. The best response-based offloading algorithm(BROA) is adopted to optimize task offloading. The simulation results show that the task latency can be decreased by 26.5’ ms, and the energy consumption in terminal users can be reduced to 66.6%. 展开更多
关键词 best response-based offloading algorithm(BROA) energy consumption mobile edge computing(MEC) mobile edge communications,computing,and caching(MEC3) task offloading
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Task offloading mechanism based on federated reinforcement learning in mobile edge computing 认领 引用 被引量：4
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作者 Jie Li Zhiping Yang +2 位作者 Xingwei Wang Yichao Xia Shijian Ni 《Digital Communications and Networks》 SCIE CSCD 2023年第2期492-504,共13页
With the arrival of 5G,latency-sensitive applications are becoming increasingly diverse.Mobile Edge Computing(MEC)technology has the characteristics of high bandwidth,low latency and low energy consumption,and has att... With the arrival of 5G,latency-sensitive applications are becoming increasingly diverse.Mobile Edge Computing(MEC)technology has the characteristics of high bandwidth,low latency and low energy consumption,and has attracted much attention among researchers.To improve the Quality of Service(QoS),this study focuses on computation offloading in MEC.We consider the QoS from the perspective of computational cost,dimensional disaster,user privacy and catastrophic forgetting of new users.The QoS model is established based on the delay and energy consumption and is based on DDQN and a Federated Learning(FL)adaptive task offloading algorithm in MEC.The proposed algorithm combines the QoS model and deep reinforcement learning algorithm to obtain an optimal offloading policy according to the local link and node state information in the channel coherence time to address the problem of time-varying transmission channels and reduce the computing energy consumption and task processing delay.To solve the problems of privacy and catastrophic forgetting,we use FL to make distributed use of multiple users’data to obtain the decision model,protect data privacy and improve the model universality.In the process of FL iteration,the communication delay of individual devices is too large,which affects the overall delay cost.Therefore,we adopt a communication delay optimization algorithm based on the unary outlier detection mechanism to reduce the communication delay of FL.The simulation results indicate that compared with existing schemes,the proposed method significantly reduces the computation cost on a device and improves the QoS when handling complex tasks. 展开更多
关键词 Mobile edge computing Task offloading QoS Deep reinforcement learning Federated learning
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Resource Allocation Method for Unmanned Aerial Vehicle-Assisted and User Cooperation Non-Linear Energy Harvesting Mobile Edge Computing System 认领 引用
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作者 HE Ximei ZHAO Yisheng +1 位作者 XU Zhihong CHEN Yong 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第6期1220-1231,共12页
Aimed at the doubly near-far problems in a large range suffered by the remote user group and in a small range existing in both nearby and remote user groups during energy harvesting and computation offloading,a resour... Aimed at the doubly near-far problems in a large range suffered by the remote user group and in a small range existing in both nearby and remote user groups during energy harvesting and computation offloading,a resource allocation method for unmanned aerial vehicle(UAV)-assisted and user cooperation non-linear energy harvesting mobile edge computing(MEC)system is proposed.The UAV equipped with an MEC server is introduced to provide energy and computing services for the remote user group to alleviate the doubly near-far problem in a large range suffered by the remote user group.The doubly near-far problem in a small range existing in both nearby and remote user groups is mitigated by user cooperation.The specific user cooperation strategy is that the user near the base station or the UAV is used as a relay to transfer the computing task of the user far from the base station or the UAV to the MEC server for computing.By jointly optimizing users’offloading time,users’transmitting power,and the hovering position of the UAV,the resource allocation problem is modeled as a nonlinear programming problem with the objective of maximizing computation efficiency.The suboptimal solution is obtained by adopting the differential evolution algorithm.Simulation results show that,compared with the resource allocation method based on genetic algorithm and the without user cooperation method,the proposed method has higher computation efficiency. 展开更多
关键词 mobile edge computing(MEC) non-linear energy harvesting unmanned aerial vehicle(UAV) user cooperation resource allocation
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Edge Cloud Selection in Mobile Edge Computing(MEC)-Aided Applications for Industrial Internet of Things(IIoT)Services 认领 引用
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作者 Dae-Young Kim SoYeon Lee +1 位作者 MinSeung Kim Seokhoon Kim 《Computer Systems Science & Engineering》 SCIE EI 2023年第11期2049-2060,共12页
In many IIoT architectures,various devices connect to the edge cloud via gateway systems.For data processing,numerous data are delivered to the edge cloud.Delivering data to an appropriate edge cloud is critical to im... In many IIoT architectures,various devices connect to the edge cloud via gateway systems.For data processing,numerous data are delivered to the edge cloud.Delivering data to an appropriate edge cloud is critical to improve IIoT service efficiency.There are two types of costs for this kind of IoT network:a communication cost and a computing cost.For service efficiency,the communication cost of data transmission should be minimized,and the computing cost in the edge cloud should be also minimized.Therefore,in this paper,the communication cost for data transmission is defined as the delay factor,and the computing cost in the edge cloud is defined as the waiting time of the computing intensity.The proposed method selects an edge cloud that minimizes the total cost of the communication and computing costs.That is,a device chooses a routing path to the selected edge cloud based on the costs.The proposed method controls the data flows in a mesh-structured network and appropriately distributes the data processing load.The performance of the proposed method is validated through extensive computer simulation.When the transition probability from good to bad is 0.3 and the transition probability from bad to good is 0.7 in wireless and edge cloud states,the proposed method reduced both the average delay and the service pause counts to about 25%of the existing method. 展开更多
关键词 Industrial Internet of Things(IIoT)network IIoT service mobile edge computing(MEC) edge cloud selection MEC-aided application
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Multi-Objective Optimization for Non-Panoramic VR in Mobile Edge Computing 认领 引用
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作者 Ren Ruimin Li Fukang +1 位作者 Dang Yaping Yang Shouyi 《China Communications》 SCIE EI CSCD 2025年第11期273-290,共18页
Non-panoramic virtual reality(VR)provides users with immersive experiences involving strong interactivity,thus attracting growing research and development attention.However,the demand for high bandwidth and low latenc... Non-panoramic virtual reality(VR)provides users with immersive experiences involving strong interactivity,thus attracting growing research and development attention.However,the demand for high bandwidth and low latency in VR services presents greater challenges to existing networks.Inspired by mobile edge computing(MEC),VR users can offload rendering tasks to other devices.The main challenge of task offloading is to minimize latency and energy consumption.Yet,in non-panoramic VR scenarios,it is essential to consider the Quality of Perceptual Experience(QOPE)for users.Simultaneously,one must also take into account the diverse requirements of users in real-world scenarios.Therefore,this paper proposes a QOPE model to measure the visual quality of non-panoramic VR users and models the non-panoramic VR task offloading problem based on MEC as a constrained multi-objective optimization problem(CMOP)that minimizes latency and energy consumption while providing a satisfied QOPE.And we propose an evolutionary algorithm(EA),GNSGA-II,to solve the CMOP.Simulation results show that the algorithm can effectively find various trade-off solutions among the objectives,satisfying the requirements of different users. 展开更多
关键词 mobile edge computing multi-objective optimization non-panoramic VR task offloading
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DDPG-Based Intelligent Computation Offloading and Resource Allocation for LEO Satellite Edge Computing Network 认领 引用 被引量：2
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作者 Jia Min Wu Jian +2 位作者 Zhang Liang Wang Xinyu Guo Qing 《China Communications》 SCIE EI CSCD 2025年第3期1-15,共15页
Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for t... Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for the global ground users.In this paper,the computation offloading problem and resource allocation problem are formulated as a mixed integer nonlinear program(MINLP)problem.This paper proposes a computation offloading algorithm based on deep deterministic policy gradient(DDPG)to obtain the user offloading decisions and user uplink transmission power.This paper uses the convex optimization algorithm based on Lagrange multiplier method to obtain the optimal MEC server resource allocation scheme.In addition,the expression of suboptimal user local CPU cycles is derived by relaxation method.Simulation results show that the proposed algorithm can achieve excellent convergence effect,and the proposed algorithm significantly reduces the system utility values at considerable time cost compared with other algorithms. 展开更多
关键词 computation offloading deep deterministic policy gradient low earth orbit satellite mobile edge computing resource allocation
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An Energy Efficient Design for UAV Communication With Mobile Edge Computing 认领 引用 被引量：13
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作者 Lingyan Fan Wu Yan +2 位作者 Xihan Chen Zhiyong Chen Qingjiang Shi 《China Communications》 SCIE CSCD 2019年第1期26-36,共11页
This paper considers a UAV communication system with mobile edge computing(MEC).We minimize the energy consumption of the whole system via jointly optimizing the UAV's trajectory and task assignment as well as CPU... This paper considers a UAV communication system with mobile edge computing(MEC).We minimize the energy consumption of the whole system via jointly optimizing the UAV's trajectory and task assignment as well as CPU's computational speed under the set of resource constrains.To this end,we first derive the energy consumption model of data processing,and then obtain the energy consumption model of fixed-wing UAV's flight.The optimization problem is mathematically formulated.To address the problem,we first obtain the approximate optimization problem by applying the technique of discrete linear state-space approximation,and then transform the non-convex constraints into convex by using linearization.Furthermore,a concave-convex procedure(CCCP) based algorithm is proposed in order to solve the optimization problem approximately.Numerical results show the efficacy of the proposed algorithm. 展开更多
关键词 mobile edge computing(MEC) UAV communication concave-convex procedure(CCCP) energy minimization
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Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing 认领 引用 被引量：37
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作者 Liang Huang Xu Feng +2 位作者 Cheng Zhang Liping Qian Yuan Wu 《Digital Communications and Networks》 SCIE 2019年第1期10-17,共8页
The rapid growth of mobile internet services has yielded a variety of computation-intensive applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which enables mobile terminals to offload comput... The rapid growth of mobile internet services has yielded a variety of computation-intensive applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which enables mobile terminals to offload computation tasks to servers located at the edge of the cellular networks, has been considered as an efficient approach to relieve the heavy computational burdens and realize an efficient computation offloading. Driven by the consequent requirement for proper resource allocations for computation offloading via MEC, in this paper, we propose a Deep-Q Network (DQN) based task offloading and resource allocation algorithm for the MEC. Specifically, we consider a MEC system in which every mobile terminal has multiple tasks offloaded to the edge server and design a joint task offloading decision and bandwidth allocation optimization to minimize the overall offloading cost in terms of energy cost, computation cost, and delay cost. Although the proposed optimization problem is a mixed integer nonlinear programming in nature, we exploit an emerging DQN technique to solve it. Extensive numerical results show that our proposed DQN-based approach can achieve the near-optimal performance。 展开更多
关键词 Mobile edge computing Joint computation offloading and resource allocation Deep-Q network
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Energy-Optimal and Delay-Bounded Computation Offloading in Mobile Edge Computing with Heterogeneous Clouds 认领 引用 被引量：28
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作者 Tianchu Zhao Sheng Zhou +3 位作者 Linqi Song Zhiyuan Jiang Xueying Guo Zhisheng Niu 《China Communications》 SCIE CSCD 2020年第5期191-210,共20页
By Mobile Edge Computing(MEC), computation-intensive tasks are offloaded from mobile devices to cloud servers, and thus the energy consumption of mobile devices can be notably reduced. In this paper, we study task off... By Mobile Edge Computing(MEC), computation-intensive tasks are offloaded from mobile devices to cloud servers, and thus the energy consumption of mobile devices can be notably reduced. In this paper, we study task offloading in multi-user MEC systems with heterogeneous clouds, including edge clouds and remote clouds. Tasks are forwarded from mobile devices to edge clouds via wireless channels, and they can be further forwarded to remote clouds via the Internet. Our objective is to minimize the total energy consumption of multiple mobile devices, subject to bounded-delay requirements of tasks. Based on dynamic programming, we propose an algorithm that minimizes the energy consumption, by jointly allocating bandwidth and computational resources to mobile devices. The algorithm is of pseudo-polynomial complexity. To further reduce the complexity, we propose an approximation algorithm with energy discretization, and its total energy consumption is proved to be within a bounded gap from the optimum. Simulation results show that, nearly 82.7% energy of mobile devices can be saved by task offloading compared with mobile device execution. 展开更多
关键词 mobile edge computing heterogeneous clouds energy saving delay bounds dynamic programming
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Intelligent Task Offloading and Collaborative Computation in Multi-UAV-Enabled Mobile Edge Computing 认领 引用 被引量：12
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作者 Jingming Xia Peng Wang +1 位作者 Bin Li Zesong Fei 《China Communications》 SCIE CSCD 2022年第4期244-256,共13页
This article establishes a three-tier mobile edge computing(MEC) network, which takes into account the cooperation between unmanned aerial vehicles(UAVs). In this MEC network, we aim to minimize the processing delay o... This article establishes a three-tier mobile edge computing(MEC) network, which takes into account the cooperation between unmanned aerial vehicles(UAVs). In this MEC network, we aim to minimize the processing delay of tasks by jointly optimizing the deployment of UAVs and offloading decisions,while meeting the computing capacity constraint of UAVs. However, the resulting optimization problem is nonconvex, which cannot be solved by general optimization tools in an effective and efficient way. To this end, we propose a two-layer optimization algorithm to tackle the non-convexity of the problem by capitalizing on alternating optimization. In the upper level algorithm, we rely on differential evolution(DE) learning algorithm to solve the deployment of the UAVs. In the lower level algorithm, we exploit distributed deep neural network(DDNN) to generate offloading decisions. Numerical results demonstrate that the two-layer optimization algorithm can effectively obtain the near-optimal deployment of UAVs and offloading strategy with low complexity. 展开更多
关键词 mobile edge computing multi-UAV collaborative cloud and edge computing deep neural network differential evolution
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Energy-Efficient Joint Caching and Transcoding for HTTP Adaptive Streaming in 5G Networks with Mobile Edge Computing 认领 引用 被引量：7
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作者 Renchao Xie Zishu Li +2 位作者 Jun Wu Qingmin Jia Tao Huang 《China Communications》 SCIE CSCD 2019年第7期229-244,共16页
With the new promising technique of mobile edge computing (MEC) emerging, by utilizing the edge computing and cloud computing capabilities to realize the HTTP adaptive video streaming transmission in MEC-based 5G netw... With the new promising technique of mobile edge computing (MEC) emerging, by utilizing the edge computing and cloud computing capabilities to realize the HTTP adaptive video streaming transmission in MEC-based 5G networks has been widely studied. Although many works have been done, most of the existing works focus on the issues of network resource utilization or the quality of experience (QoE) promotion, while the energy efficiency is largely ignored. In this paper, different from previous works, in order to realize the energy efficiency for video transmission in MEC-enhanced 5G networks, we propose a joint caching and transcoding schedule strategy for HTTP adaptive video streaming transmission by taking the caching and transcoding into consideration. We formulate the problem of energy-efficient joint caching and transcoding as an integer programming problem to minimize the system energy consumption. Due to solving the optimization problem brings huge computation complexity, therefore, to make the optimization problem tractable, a heuristic algorithm based on simulated annealing algorithm is proposed to iteratively reach the global optimum solution with a lower complexity and higher accuracy. Finally, numerical simulation results are illustrated to demonstrated that our proposed scheme brings an excellent performance. 展开更多
关键词 mobile edge computing HTTP adaptive streaming caching transcoding energy efficiency
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A Greedy Algorithm for Task Offloading in Mobile Edge Computing System 认领 引用 被引量：38
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作者 Feng Wei Sixuan Chen Weixia Zou 《China Communications》 SCIE CSCD 2018年第11期149-157,共9页
Mobile edge computing （MEC） is a novel technique that can reduce mobiles＇ com- putational burden by tasks offioading, which emerges as a promising paradigm to provide computing capabilities in close proximity to mo... Mobile edge computing （MEC） is a novel technique that can reduce mobiles＇ com- putational burden by tasks offioading, which emerges as a promising paradigm to provide computing capabilities in close proximity to mobile users. In this paper, we will study the scenario where multiple mobiles upload tasks to a MEC server in a sing cell, and allocating the limited server resources and wireless chan- nels between mobiles becomes a challenge. We formulate the optimization problem for the energy saved on mobiles with the tasks being dividable, and utilize a greedy choice to solve the problem. A Select Maximum Saved Energy First （SMSEF） algorithm is proposed to realize the solving process. We examined the saved energy at different number of nodes and channels, and the results show that the proposed scheme can effectively help mobiles to save energy in the MEC system. 展开更多
关键词 mobile edge computing task off- loading greedy choice energy resource allo- cation
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UAV-assisted cooperative offloading energy efficiency system for mobile edge computing 认领 引用 被引量：2
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作者 Xue-Yong Yu Wen-Jin Niu +1 位作者 Ye Zhu Hong-Bo Zhu 《Digital Communications and Networks》 SCIE CSCD 2024年第1期16-24,共9页
Reliable communication and intensive computing power cannot be provided effectively by temporary hot spots in disaster areas and complex terrain ground infrastructure.Mitigating this has greatly developed the applicat... Reliable communication and intensive computing power cannot be provided effectively by temporary hot spots in disaster areas and complex terrain ground infrastructure.Mitigating this has greatly developed the application and integration of UAV and Mobile Edge Computing(MEC)to the Internet of Things(loT).However,problems such as multi-user and huge data flow in large areas,which contradict the reality that a single UAV is constrained by limited computing power,still exist.Due to allowing UAV collaboration to accomplish complex tasks,cooperative task offloading between multiple UAVs must meet the interdependence of tasks and realize parallel processing,which reduces the computing power consumption and endurance pressure of terminals.Considering the computing requirements of the user terminal,delay constraint of a computing task,energy constraint,and safe distance of UAV,we constructed a UAV-Assisted cooperative offloading energy efficiency system for mobile edge computing to minimize user terminal energy consumption.However,the resulting optimization problem is originally nonconvex and thus,difficult to solve optimally.To tackle this problem,we developed an energy efficiency optimization algorithm using Block Coordinate Descent(BCD)that decomposes the problem into three convex subproblems.Furthermore,we jointly optimized the number of local computing tasks,number of computing offloaded tasks,trajectories of UAV,and offloading matching relationship between multi-UAVs and multiuser terminals.Simulation results show that the proposed approach is suitable for different channel conditions and significantly saves the user terminal energy consumption compared with other benchmark schemes. 展开更多
关键词 Computation offloading Internet of things(IoT) Mobile edge computing(MEC) Block coordinate descent(BCD)
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Recent advances in mobile edge computing and content caching 认领 引用 被引量：14
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作者 Sunitha Safavat Naveen Naik Sapavath Danda B.Rawat 《Digital Communications and Networks》 SCIE EI 2020年第2期189-194,共6页
The demand for digital media services is increasing as the number of wireless subscriptions is growing exponentially.In order to meet this growing need,mobile wireless networks have been advanced at a tremendous pace ... The demand for digital media services is increasing as the number of wireless subscriptions is growing exponentially.In order to meet this growing need,mobile wireless networks have been advanced at a tremendous pace over recent days.However,the centralized architecture of existing mobile networks,with limited capacity and range of bandwidth of the radio access network and low bandwidth back-haul network,can not handle the exponentially increasing mobile traffic.Recently,we have seen the growth of new mechanisms of data caching and delivery methods through intermediate caching servers.In this paper,we present a survey on recent advances in mobile edge computing and content caching,including caching insertion and expulsion policies,the behavior of the caching system,and caching optimization based on wireless networks.Some of the important open challenges in mobile edge computing with content caching are identified and discussed.We have also compared edge,fog and cloud computing in terms of delay.Readers of this paper will get a thorough understanding of recent advances in mobile edge computing and content caching in mobile wireless networks. 展开更多
关键词 Mobile edge computing Content caching MEC
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Online Computation Offloading and Trajectory Scheduling for UAV-Enabled Wireless Powered Mobile Edge Computing 认领 引用 被引量：6
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作者 Han Hu Xiang Zhou +1 位作者 Qun Wang Rose Qingyang Hu 《China Communications》 SCIE CSCD 2022年第4期257-273,共17页
The unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) architecture is expected to be a powerful technique to facilitate 5 G and beyond ubiquitous wireless connectivity and diverse vertical applications a... The unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) architecture is expected to be a powerful technique to facilitate 5 G and beyond ubiquitous wireless connectivity and diverse vertical applications and services, anytime and anywhere. Wireless power transfer(WPT) is another promising technology to prolong the operation time of low-power wireless devices in the era of Internet of Things(IoT). However, the integration of WPT and UAV-enabled MEC systems is far from being well studied, especially in dynamic environments. In order to tackle this issue, this paper aims to investigate the stochastic computation offloading and trajectory scheduling for the UAV-enabled wireless powered MEC system. A UAV offers both RF wireless power transmission and computation services for IoT devices. Considering the stochastic task arrivals and random channel conditions, a long-term average energyefficiency(EE) minimization problem is formulated.Due to non-convexity and the time domain coupling of the variables in the formulated problem, a lowcomplexity online computation offloading and trajectory scheduling algorithm(OCOTSA) is proposed by exploiting Lyapunov optimization. Simulation results verify that there exists a balance between EE and the service delay, and demonstrate that the system EE performance obtained by the proposed scheme outperforms other benchmark schemes. 展开更多
关键词 energy efficiency mobile edge computing UAV-enabled wireless power transfer trajectorys cheduling
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