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
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
In the era of Internet of Things(Io T),mobile edge computing(MEC)and wireless power transfer(WPT)provide a prominent solution for computation-intensive applications to enhance computation capability and achieve sustai...In the era of Internet of Things(Io T),mobile edge computing(MEC)and wireless power transfer(WPT)provide a prominent solution for computation-intensive applications to enhance computation capability and achieve sustainable energy supply.A wireless-powered mobile edge computing(WPMEC)system consisting of a hybrid access point(HAP)combined with MEC servers and many users is considered in this paper.In particular,a novel multiuser cooperation scheme based on orthogonal frequency division multiple access(OFDMA)is provided to improve the computation performance,where users can split the computation tasks into various parts for local computing,offloading to corresponding helper,and HAP for remote execution respectively with the aid of helper.Specifically,we aim at maximizing the weighted sum computation rate(WSCR)by optimizing time assignment,computation-task allocation,and transmission power at the same time while keeping energy neutrality in mind.We transform the original non-convex optimization problem to a convex optimization problem and then obtain a semi-closed form expression of the optimal solution by considering the convex optimization techniques.Simulation results demonstrate that the proposed multi-user cooperationassisted WPMEC scheme greatly improves the WSCR of all users than the existing schemes.In addition,OFDMA protocol increases the fairness and decreases delay among the users when compared to TDMA protocol.展开更多
In this paper,we study an Intelligent Reflecting Surface(IRS)assisted Mobile Edge Computing(MEC)system under eavesdropping threats,where the IRS is used to enhance the energy signal transmission and the offloading per...In this paper,we study an Intelligent Reflecting Surface(IRS)assisted Mobile Edge Computing(MEC)system under eavesdropping threats,where the IRS is used to enhance the energy signal transmission and the offloading performance between Wireless Devices(WDs)and the Access Point(AP).Specifically,in the proposed scheme,the AP first powers all WDs with the wireless power transfer through both direct and IRS-assisted links.Then,powered by the harvested energy,all WDs securely offload their computation tasks through the two links in the time division multiple access mode.To determine the local and offloading computational bits,we formulate an optimization problem to jointly design the IRS's phase shift and allocate the time slots constrained by the security and energy requirements.To cope with this non-convex optimization problem,we adopt semidefinite relaxations,singular value decomposition techniques,and Lagrange dual method.Moreover,we propose a dichotomy particle swarm algorithm based on the bisection method to process the overall optimization problem and improve the convergence speed.The numerical results illustrate that the proposed scheme can boost the performance of MEC and secure computation rates compared with other IRS-assisted MEC benchmark schemes.展开更多
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
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 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
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
In this paper,we study the system performance of mobile edge computing(MEC)wireless sensor networks(WSNs)using a multiantenna access point(AP)and two sensor clusters based on uplink nonorthogonal multiple access(NOMA)...In this paper,we study the system performance of mobile edge computing(MEC)wireless sensor networks(WSNs)using a multiantenna access point(AP)and two sensor clusters based on uplink nonorthogonal multiple access(NOMA).Due to limited computation and energy resources,the cluster heads(CHs)offload their tasks to a multiantenna AP over Nakagami-m fading.We proposed a combination protocol for NOMA-MEC-WSNs in which the AP selects either selection combining(SC)or maximal ratio combining(MRC)and each cluster selects a CH to participate in the communication process by employing the sensor node(SN)selection.We derive the closed-form exact expressions of the successful computation probability(SCP)to evaluate the system performance with the latency and energy consumption constraints of the considered WSN.Numerical results are provided to gain insight into the system performance in terms of the SCP based on system parameters such as the number of AP antennas,number of SNs in each cluster,task length,working frequency,offloading ratio,and transmit power allocation.Furthermore,to determine the optimal resource parameters,i.e.,the offloading ratio,power allocation of the two CHs,and MEC AP resources,we proposed two algorithms to achieve the best system performance.Our approach reveals that the optimal parameters with different schemes significantly improve SCP compared to other similar studies.We use Monte Carlo simulations to confirm the validity of our analysis.展开更多
Mobile edge computing (MEC) has a vital role in various delay-sensitive applications. With the increasing popularity of low-computing-capability Internet of Things (IoT) devices in industry 4.0 technology, MEC also fa...Mobile edge computing (MEC) has a vital role in various delay-sensitive applications. With the increasing popularity of low-computing-capability Internet of Things (IoT) devices in industry 4.0 technology, MEC also facilitates wireless power transfer, enhancing efficiency and sustainability for these devices. The most related studies concerning the computation rate in MEC are based on the coordinate descent method, the alternating direction method of multipliers (ADMMs) and Lyapunov optimization. Nevertheless, these studies do not consider the buffer queue size. This research work concerns the computation rate maximization for wireless-powered and multiple-user MEC systems, specifically focusing on the computation rate of end devices and managing the task buffer queue before computation at the terminal devices. A deep reinforcement learning (RL)-based task offloading algorithm is proposed to maximize the computation rate of end devices and minimizes the buffer queue size at the terminal devices.Precisely, considering the channel gain, the buffer queue size and wireless power transfer, it further formalizes the task offloading problem. The mode selection for task offloading is based on the individual channel gain, the buffer queue size and wireless power transfer maximization in a particular time slot.The central idea of this work is to explore the best optimal mode selection for IoT devices connected to the MEC system. The proposed algorithm optimizes computation delay by maximizing the computation rate of end devices and minimizing the buffer queue size before computation at the terminal devices. Then, the current study presents a deep RL-based task offloading algorithm to solve such a mixed-integer and non-convex optimization problem, aiming to get a better trade-off between the buffer queue size and the computation rate. The extensive simulation results reveal that the presented algorithm is much more efficient than the existing work to maintain a small buffer queue for terminal devices while simultaneously achieving a high-level computation rate.展开更多
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
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
Through enabling the IT and cloud computation capacities at Radio Access Network(RAN),Mobile Edge Computing(MEC) makes it possible to deploy and provide services locally.Therefore,MEC becomes the potential technology ...Through enabling the IT and cloud computation capacities at Radio Access Network(RAN),Mobile Edge Computing(MEC) makes it possible to deploy and provide services locally.Therefore,MEC becomes the potential technology to satisfy the requirements of 5G network to a certain extent,due to its functions of services localization,local breakout,caching,computation offloading,network context information exposure,etc.Especially,MEC can decrease the end-to-end latency dramatically through service localization and caching,which is key requirement of 5G low latency scenario.However,the performance of MEC still needs to be evaluated and verified for future deployment.Thus,the concept of MEC is introduced into5 G architecture and analyzed for different 5G scenarios in this paper.Secondly,the evaluation of MEC performance is conducted and analyzed in detail,especially for network end-to-end latency.In addition,some challenges of the MEC are also discussed for future deployment.展开更多
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
Mobile Edge Computing(MEC)-based computation offloading is a promising application paradigm for serving large numbers of users with various delay and energy requirements.In this paper,we propose a flexible MECbased re...Mobile Edge Computing(MEC)-based computation offloading is a promising application paradigm for serving large numbers of users with various delay and energy requirements.In this paper,we propose a flexible MECbased requirement-adaptive partial offloading model to accommodate each user's specific preference regarding delay and energy consumption.To address the dimensional differences between time and energy,we introduce two normalized parameters and then derive the computational overhead of processing tasks.Different from existing works,this paper considers practical variations in the user request patterns,and exploits a flexible partial offloading mode to minimize computation overheads subject to tolerable delay,task workload and power constraints.Since the resulting problem is non-convex,we decouple it into two convex subproblems and present an iterative algorithm to obtain a feasible offloading solution.Numerical experiments show that our proposed scheme achieves a significant improvement in computation overheads compared with existing schemes.展开更多
The development of intelligent connected vehicles(ICVs)has tremendously inspired the emergence of a new computing paradigm called mobile edge computing(MEC),which meets the demands of delay-sensitive on-vehicle applic...The development of intelligent connected vehicles(ICVs)has tremendously inspired the emergence of a new computing paradigm called mobile edge computing(MEC),which meets the demands of delay-sensitive on-vehicle applications.Most existing studies focusing on the issue of task offloading in ICVs assume that the MEC server can directly complete computation tasks without considering the necessity of service caching.However,this is unrealistic in practice because a large number of tasks require the use of corresponding third-party libraries and databases,that is,service caching.Therefore,we investigate the delay optimization in an MEC-enabled ICVs system with multiple mobile vehicles,resource-limited base stations(BSs),and one cloud server.We aim to determine the optimal service caching and task offloading decisions to minimize the overall system delay using mixed-integer nonlinear programming.To address this problem,we first convert it into a quadratically constrained quadratic program and then propose an efficient semidefinite relaxation-based joint service caching and task offloading(JSCTO)algorithm to obtain the service caching and task offloading decisions.In the simulations,we validate the efficiency of our proposed method by setting different numbers of vehicles and the storage capacity of BSs.The results show that our proposed JSCTO algorithm can significantly decrease the total delay of all offloaded tasks compared with the cloud processing only scheme.展开更多
Efficient response speed and information processing speed are among the characteristics of mobile edge computing(MEC).However,MEC easily causes information leakage and loss problems because it requires frequent data e...Efficient response speed and information processing speed are among the characteristics of mobile edge computing(MEC).However,MEC easily causes information leakage and loss problems because it requires frequent data exchange.This work proposes an anonymous privacy data protection and access control scheme based on elliptic curve cryptography(ECC)and bilinear pairing to protect the communication security of the MEC.In the proposed scheme,the information sender encrypts private information through the ECC algorithm,and the information receiver uses its own key information and bilinear pairing to extract and verify the identity of the information sender.During each round of communication,the proposed scheme uses timestamps and random numbers to ensure the freshness of each round of conversation.Experimental results show that the proposed scheme has good security performance and can provide data privacy protection,integrity verification,and traceability for the communication process of MEC.The proposed scheme has a lower cost than other related schemes.The communication and computational cost of the proposed scheme are reduced by 31.08% and 22.31% on average compared with those of the other related schemes.展开更多
In this paper,we co-design the transmission power and the offloading strategy for job offloading to a mobile edge computing(MEC)server at Terahertz(THz)frequencies.The goal is to minimize the communication energy cons...In this paper,we co-design the transmission power and the offloading strategy for job offloading to a mobile edge computing(MEC)server at Terahertz(THz)frequencies.The goal is to minimize the communication energy consumption while providing ultra-reliable low end-to-end latency(URLLC)services.To that end,we first establish a novel reliability framework,where the end-to-end(E2E)delay equals a weighted sum of the local computing delay,the communication delay and the edge computing delay,and the reliability is defined as the probability that the E2E delay remains below a certain pre-defined threshold.This reliability gives a full view of the statistics of the E2E delay,thus constituting advancement over prior works that have considered only average delays.Based on this framework,we establish the communication energy consumption minimization problem under URLLC constraints.This optimization problem is non-convex.To handle that issue,we first consider the special single-user case,where we derive the optimal solution by analyzing the structure of the optimization problem.Further,based on the analytical result for the single-user case,we decouple the optimization problem for multi-user scenarios into several sub-optimization problems and propose a sub-optimal algorithm to solve it.Numerical results verify the performance of the proposed algorithm.展开更多
Nowadays,with the widespread application of the Internet of Things(IoT),mobile devices are renovating our lives.The data generated by mobile devices has reached a massive level.The traditional centralized processing i...Nowadays,with the widespread application of the Internet of Things(IoT),mobile devices are renovating our lives.The data generated by mobile devices has reached a massive level.The traditional centralized processing is not suitable for processing the data due to limited computing power and transmission load.Mobile Edge Computing(MEC)has been proposed to solve these problems.Because of limited computation ability and battery capacity,tasks can be executed in the MEC server.However,how to schedule those tasks becomes a challenge,and is the main topic of this piece.In this paper,we design an efficient intelligent algorithm to jointly optimize energy cost and computing resource allocation in MEC.In view of the advantages of deep learning,we propose a Deep Learning-Based Traffic Scheduling Approach(DLTSA).We translate the scheduling problem into a classification problem.Evaluation demonstrates that our DLTSA approach can reduce energy cost and have better performance compared to traditional scheduling algorithms.展开更多
In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of ...In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of UAV,the transmitting beamforming of users,and the phase shift matrix of IRS.The original problem is strong non-convex and difficult to solve.We first propose two basic modes of the proactive eavesdropper,and obtain the closed-form solution for the boundary conditions of the two modes.Then we transform the original problem into an equivalent one and propose an alternating optimization(AO)based method to obtain a local optimal solution.The convergence of the algorithm is illustrated by numerical results.Further,we propose a zero forcing(ZF)based method as sub-optimal solution,and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes.展开更多
基金the National Natural Science Foundation of China(No.61871133)the Natural Science Foundation of Fujian Province(No.2021J01587)。
文摘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.
基金supported in part by the U.S. National Science Foundation under Grant CNS-2007995in part by the National Natural Science Foundation of China under Grant 92067201,62171231in part by Jiangsu Provincial Key Research and Development Program under Grant BE2020084-1。
文摘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.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant No.62071306in part by Shenzhen Science and Technology Program under Grants JCYJ20200109113601723,JSGG20210802154203011 and JSGG20210420091805014。
文摘In the era of Internet of Things(Io T),mobile edge computing(MEC)and wireless power transfer(WPT)provide a prominent solution for computation-intensive applications to enhance computation capability and achieve sustainable energy supply.A wireless-powered mobile edge computing(WPMEC)system consisting of a hybrid access point(HAP)combined with MEC servers and many users is considered in this paper.In particular,a novel multiuser cooperation scheme based on orthogonal frequency division multiple access(OFDMA)is provided to improve the computation performance,where users can split the computation tasks into various parts for local computing,offloading to corresponding helper,and HAP for remote execution respectively with the aid of helper.Specifically,we aim at maximizing the weighted sum computation rate(WSCR)by optimizing time assignment,computation-task allocation,and transmission power at the same time while keeping energy neutrality in mind.We transform the original non-convex optimization problem to a convex optimization problem and then obtain a semi-closed form expression of the optimal solution by considering the convex optimization techniques.Simulation results demonstrate that the proposed multi-user cooperationassisted WPMEC scheme greatly improves the WSCR of all users than the existing schemes.In addition,OFDMA protocol increases the fairness and decreases delay among the users when compared to TDMA protocol.
基金supported in part by the National Natural Science Foundation of China under Grant 62271399 and 62206221in part by the Key Research and Development Program of Shaanxi Province under Grant 2022KW-07+1 种基金in part by National Key Research and Development Program of China under Grant 2020YFB1807003in part by the Shanghai Sailing Program under Grant 20YF1416700。
文摘In this paper,we study an Intelligent Reflecting Surface(IRS)assisted Mobile Edge Computing(MEC)system under eavesdropping threats,where the IRS is used to enhance the energy signal transmission and the offloading performance between Wireless Devices(WDs)and the Access Point(AP).Specifically,in the proposed scheme,the AP first powers all WDs with the wireless power transfer through both direct and IRS-assisted links.Then,powered by the harvested energy,all WDs securely offload their computation tasks through the two links in the time division multiple access mode.To determine the local and offloading computational bits,we formulate an optimization problem to jointly design the IRS's phase shift and allocate the time slots constrained by the security and energy requirements.To cope with this non-convex optimization problem,we adopt semidefinite relaxations,singular value decomposition techniques,and Lagrange dual method.Moreover,we propose a dichotomy particle swarm algorithm based on the bisection method to process the overall optimization problem and improve the convergence speed.The numerical results illustrate that the proposed scheme can boost the performance of MEC and secure computation rates compared with other IRS-assisted MEC benchmark schemes.
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 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the National S&T Major Project (No. 2018ZX03001011)the National Key R&D Program(No.2018YFB1801102)+1 种基金the National Natural Science Foundation of China (No. 61671072)the Beijing Natural Science Foundation (No. L192025)
文摘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%.
基金supported by the Jiangsu Provincial Key Research and Development Program(No.BE2020084-4)the National Natural Science Foundation of China(No.92067201)+2 种基金the National Natural Science Foundation of China(61871446)the Open Research Fund of Jiangsu Key Laboratory of Wireless Communications(710020017002)the Natural Science Foundation of Nanjing University of Posts and telecommunications(NY220047).
文摘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.
基金supported in part by Thailand Science Research and Innovation(TSRI)and National Research Council of Thailand(NRCT)via International Research Network Program(IRN61W0006)Thailand+1 种基金by Khon Kaen University,ThailandDuy Tan University,Vietnam。
文摘In this paper,we study the system performance of mobile edge computing(MEC)wireless sensor networks(WSNs)using a multiantenna access point(AP)and two sensor clusters based on uplink nonorthogonal multiple access(NOMA).Due to limited computation and energy resources,the cluster heads(CHs)offload their tasks to a multiantenna AP over Nakagami-m fading.We proposed a combination protocol for NOMA-MEC-WSNs in which the AP selects either selection combining(SC)or maximal ratio combining(MRC)and each cluster selects a CH to participate in the communication process by employing the sensor node(SN)selection.We derive the closed-form exact expressions of the successful computation probability(SCP)to evaluate the system performance with the latency and energy consumption constraints of the considered WSN.Numerical results are provided to gain insight into the system performance in terms of the SCP based on system parameters such as the number of AP antennas,number of SNs in each cluster,task length,working frequency,offloading ratio,and transmit power allocation.Furthermore,to determine the optimal resource parameters,i.e.,the offloading ratio,power allocation of the two CHs,and MEC AP resources,we proposed two algorithms to achieve the best system performance.Our approach reveals that the optimal parameters with different schemes significantly improve SCP compared to other similar studies.We use Monte Carlo simulations to confirm the validity of our analysis.
基金National Natural Science Foundation of China(No.61902060)Shanghai Sailing Program,China(No.19YF1402100)+1 种基金Fundamental Research Funds for the Central Universities,China(No.2232019D3-51)Open Foundation of State Key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications,China)(No.SKLNST-2021-1-06)。
文摘Mobile edge computing (MEC) has a vital role in various delay-sensitive applications. With the increasing popularity of low-computing-capability Internet of Things (IoT) devices in industry 4.0 technology, MEC also facilitates wireless power transfer, enhancing efficiency and sustainability for these devices. The most related studies concerning the computation rate in MEC are based on the coordinate descent method, the alternating direction method of multipliers (ADMMs) and Lyapunov optimization. Nevertheless, these studies do not consider the buffer queue size. This research work concerns the computation rate maximization for wireless-powered and multiple-user MEC systems, specifically focusing on the computation rate of end devices and managing the task buffer queue before computation at the terminal devices. A deep reinforcement learning (RL)-based task offloading algorithm is proposed to maximize the computation rate of end devices and minimizes the buffer queue size at the terminal devices.Precisely, considering the channel gain, the buffer queue size and wireless power transfer, it further formalizes the task offloading problem. The mode selection for task offloading is based on the individual channel gain, the buffer queue size and wireless power transfer maximization in a particular time slot.The central idea of this work is to explore the best optimal mode selection for IoT devices connected to the MEC system. The proposed algorithm optimizes computation delay by maximizing the computation rate of end devices and minimizing the buffer queue size before computation at the terminal devices. Then, the current study presents a deep RL-based task offloading algorithm to solve such a mixed-integer and non-convex optimization problem, aiming to get a better trade-off between the buffer queue size and the computation rate. The extensive simulation results reveal that the presented algorithm is much more efficient than the existing work to maintain a small buffer queue for terminal devices while simultaneously achieving a high-level computation rate.
基金supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (No.2021R1C1C1013133)supported by the Institute of Information and Communications Technology Planning and Evaluation (IITP)grant funded by the Korea Government (MSIT) (RS-2022-00167197,Development of Intelligent 5G/6G Infrastructure Technology for The Smart City)supported by the Soonchunhyang University Research Fund.
文摘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.
基金supported in part by National Natural Science Foundation of China(Grant No.61702149,U1709220)
文摘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.
基金supported by the National High Technology Research and Development Program(863) of China(No.2015AA01A701)
文摘Through enabling the IT and cloud computation capacities at Radio Access Network(RAN),Mobile Edge Computing(MEC) makes it possible to deploy and provide services locally.Therefore,MEC becomes the potential technology to satisfy the requirements of 5G network to a certain extent,due to its functions of services localization,local breakout,caching,computation offloading,network context information exposure,etc.Especially,MEC can decrease the end-to-end latency dramatically through service localization and caching,which is key requirement of 5G low latency scenario.However,the performance of MEC still needs to be evaluated and verified for future deployment.Thus,the concept of MEC is introduced into5 G architecture and analyzed for different 5G scenarios in this paper.Secondly,the evaluation of MEC performance is conducted and analyzed in detail,especially for network end-to-end latency.In addition,some challenges of the MEC are also discussed for future deployment.
基金This work is partly supported by the US NSF under grants CNS 1650831,and HRD 1828811the U.S.Department of Homeland Security under grant DHS 2017-ST-062-000003.
文摘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.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 62171113 and 61941113in part by the Fundamental Research Funds for the Central Universities under Grant N2116003 and N2116011.
文摘Mobile Edge Computing(MEC)-based computation offloading is a promising application paradigm for serving large numbers of users with various delay and energy requirements.In this paper,we propose a flexible MECbased requirement-adaptive partial offloading model to accommodate each user's specific preference regarding delay and energy consumption.To address the dimensional differences between time and energy,we introduce two normalized parameters and then derive the computational overhead of processing tasks.Different from existing works,this paper considers practical variations in the user request patterns,and exploits a flexible partial offloading mode to minimize computation overheads subject to tolerable delay,task workload and power constraints.Since the resulting problem is non-convex,we decouple it into two convex subproblems and present an iterative algorithm to obtain a feasible offloading solution.Numerical experiments show that our proposed scheme achieves a significant improvement in computation overheads compared with existing schemes.
基金the National Natural Science Foundation of China(Nos.61772130 and 62072096)the Fundamental Research Funds for the Central Universities(No.2232020A-12)+1 种基金the International S&T Cooperation Program of Shanghai Science and Technology Commission(No.20220713000)the Young Top-Notch Talent Program in Shanghai。
文摘The development of intelligent connected vehicles(ICVs)has tremendously inspired the emergence of a new computing paradigm called mobile edge computing(MEC),which meets the demands of delay-sensitive on-vehicle applications.Most existing studies focusing on the issue of task offloading in ICVs assume that the MEC server can directly complete computation tasks without considering the necessity of service caching.However,this is unrealistic in practice because a large number of tasks require the use of corresponding third-party libraries and databases,that is,service caching.Therefore,we investigate the delay optimization in an MEC-enabled ICVs system with multiple mobile vehicles,resource-limited base stations(BSs),and one cloud server.We aim to determine the optimal service caching and task offloading decisions to minimize the overall system delay using mixed-integer nonlinear programming.To address this problem,we first convert it into a quadratically constrained quadratic program and then propose an efficient semidefinite relaxation-based joint service caching and task offloading(JSCTO)algorithm to obtain the service caching and task offloading decisions.In the simulations,we validate the efficiency of our proposed method by setting different numbers of vehicles and the storage capacity of BSs.The results show that our proposed JSCTO algorithm can significantly decrease the total delay of all offloaded tasks compared with the cloud processing only scheme.
基金partially supported by the National Natural Science Foundation of China under Grant 62072170 and Grant 62177047the Fundamental Research Funds for the Central Universities under Grant 531118010527+1 种基金the Science and Technology Key Projects of Hunan Province under Grant 2022GK2015the Hunan Provincial Natural Science Foundation of China under Grant 2021JJ30141.
文摘Efficient response speed and information processing speed are among the characteristics of mobile edge computing(MEC).However,MEC easily causes information leakage and loss problems because it requires frequent data exchange.This work proposes an anonymous privacy data protection and access control scheme based on elliptic curve cryptography(ECC)and bilinear pairing to protect the communication security of the MEC.In the proposed scheme,the information sender encrypts private information through the ECC algorithm,and the information receiver uses its own key information and bilinear pairing to extract and verify the identity of the information sender.During each round of communication,the proposed scheme uses timestamps and random numbers to ensure the freshness of each round of conversation.Experimental results show that the proposed scheme has good security performance and can provide data privacy protection,integrity verification,and traceability for the communication process of MEC.The proposed scheme has a lower cost than other related schemes.The communication and computational cost of the proposed scheme are reduced by 31.08% and 22.31% on average compared with those of the other related schemes.
文摘In this paper,we co-design the transmission power and the offloading strategy for job offloading to a mobile edge computing(MEC)server at Terahertz(THz)frequencies.The goal is to minimize the communication energy consumption while providing ultra-reliable low end-to-end latency(URLLC)services.To that end,we first establish a novel reliability framework,where the end-to-end(E2E)delay equals a weighted sum of the local computing delay,the communication delay and the edge computing delay,and the reliability is defined as the probability that the E2E delay remains below a certain pre-defined threshold.This reliability gives a full view of the statistics of the E2E delay,thus constituting advancement over prior works that have considered only average delays.Based on this framework,we establish the communication energy consumption minimization problem under URLLC constraints.This optimization problem is non-convex.To handle that issue,we first consider the special single-user case,where we derive the optimal solution by analyzing the structure of the optimization problem.Further,based on the analytical result for the single-user case,we decouple the optimization problem for multi-user scenarios into several sub-optimization problems and propose a sub-optimal algorithm to solve it.Numerical results verify the performance of the proposed algorithm.
基金supported in part by the National Natural Science Foun-dation of China(61902029)R&D Program of Beijing Municipal Education Commission(No.KM202011232015)Project for Acceleration of University Classi cation Development(Nos.5112211036,5112211037,5112211038).
文摘Nowadays,with the widespread application of the Internet of Things(IoT),mobile devices are renovating our lives.The data generated by mobile devices has reached a massive level.The traditional centralized processing is not suitable for processing the data due to limited computing power and transmission load.Mobile Edge Computing(MEC)has been proposed to solve these problems.Because of limited computation ability and battery capacity,tasks can be executed in the MEC server.However,how to schedule those tasks becomes a challenge,and is the main topic of this piece.In this paper,we design an efficient intelligent algorithm to jointly optimize energy cost and computing resource allocation in MEC.In view of the advantages of deep learning,we propose a Deep Learning-Based Traffic Scheduling Approach(DLTSA).We translate the scheduling problem into a classification problem.Evaluation demonstrates that our DLTSA approach can reduce energy cost and have better performance compared to traditional scheduling algorithms.
基金This work was supported by the Key Scientific and Technological Project of Henan Province(Grant Number 222102210212)Doctoral Research Start Project of Henan Institute of Technology(Grant Number KQ2005)Key Research Projects of Colleges and Universities in Henan Province(Grant Number 23B510006).
文摘In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of UAV,the transmitting beamforming of users,and the phase shift matrix of IRS.The original problem is strong non-convex and difficult to solve.We first propose two basic modes of the proactive eavesdropper,and obtain the closed-form solution for the boundary conditions of the two modes.Then we transform the original problem into an equivalent one and propose an alternating optimization(AO)based method to obtain a local optimal solution.The convergence of the algorithm is illustrated by numerical results.Further,we propose a zero forcing(ZF)based method as sub-optimal solution,and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes.
