IRS Assisted UAV Communications against Proactive Eavesdropping in Mobile Edge Computing Networks

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.

UAV-assisted cooperative offloading energy efficiency system for mobile edge computing

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.

Age of Information Based User Scheduling and Data Assignment in Multi-User Mobile Edge Computing Networks:An Online Algorithm

This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data assignment.Due to the non-analytic expression of the WSA w.r.t.the optimization variables and the unknowability of future network information,the problem cannot be solved with known solution methods.Therefore,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems separately.We analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of users.Simulation results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline methods.It is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.

Energy Efficiency Maximization in Mobile Edge Computing Networks via IRS assisted UAV Communications

In this paper,we investigate the energy efficiency maximization for mobile edge computing(MEC)in intelligent reflecting surface(IRS)assisted unmanned aerial vehicle(UAV)communications.In particular,UAVcan collect the computing tasks of the terrestrial users and transmit the results back to them after computing.We jointly optimize the users’transmitted beamforming and uploading ratios,the phase shift matrix of IRS,and the UAV trajectory to improve the energy efficiency.The formulated optimization problem is highly non-convex and difficult to be solved directly.Therefore,we decompose the original problem into three sub-problems.We first propose the successive convex approximation(SCA)based method to design the beamforming of the users and the phase shift matrix of IRS,and apply the Lagrange dual method to obtain a closed-form expression of the uploading ratios.For the trajectory optimization,we propose a block coordinate descent(BCD)based method to obtain a local optimal solution.Finally,we propose the alternating optimization(AO)based overall algorithmand analyzed its complexity to be equivalent or lower than existing algorithms.Simulation results show the superiority of the proposedmethod compared with existing schemes in energy efficiency.

Edge Cloud Selection in Mobile Edge Computing(MEC)-Aided Applications for Industrial Internet of Things(IIoT)Services

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.

Adaptive delay-energy balanced partial offloading strategy in Mobile Edge Computing networks

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.

Intelligent Traffic Scheduling for Mobile Edge Computing in IoT via Deep Learning

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.

Computation Rate Maximization in Multi-User Cooperation-Assisted Wireless-Powered Mobile Edge Computing with OFDMA

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.

Mobile Edge Communications, Computing, and Caching(MEC3) Technology in the Maritime Communication Network

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%.

Task Offloading and Resource Allocation for Edge-Enabled Mobile Learning

Mobile learning has evolved into a new format of education based on communication and computer technology that is favored by an increasing number of learning users thanks to the development of wireless communication networks,mobile edge computing,artificial intelligence,and mobile devices.However,due to the constrained data processing capacity of mobile devices,efficient and effective interactive mobile learning is a challenge.Therefore,for mobile learning,we propose a"Cloud,Edge and End"fusion system architecture.Through task offloading and resource allocation for edge-enabled mobile learning to reduce the time and energy consumption of user equipment.Then,we present the proposed solutions that uses the minimum cost maximum flow(MCMF)algorithm to deal with the offloading problem and the deep Q network(DQN)algorithm to deal with the resource allocation problem respectively.Finally,the performance evaluation shows that the proposed offloading and resource allocation scheme can improve system performance,save energy,and satisfy the needs of learning users.

An Energy Efficient Design for UAV Communication With Mobile Edge Computing

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.

基于改进人工蜂鸟算法的MEC任务卸载策略

面对信息化网络环境中大量时延敏感型和计算密集型任务的计算需求,移动边缘计算(MEC)及其计算卸载技术提供了一种行之有效的解决方案。针对资源受限移动边缘系统的任务卸载策略,设计一种成本最优化算法。首先,结合系统的基本数据构建多用户多服务器网络场景,并根据时延、能耗等待优化指标建立一种包含惩罚项的最小化成本优化模型;然后,提出一种改进人工蜂鸟算法(IAHA),通过对原算法的寻优方式与算法结构进行适应性地调整和优化,并引入一种紧急避险策略,实现系统模型与算法映射的高度契合以及对模型问题快速精确求解,进而得到系统的最优卸载策略;最后,应用策略进行部署以降低系统的成本支出和提升用户的服务体验。仿真实验结果表明,所提改进算法能够有效降低系统成本,并且在针对高维复杂模型求解时具有更突出的收敛性能和寻优精度,在特定实验条件下,所提改进算法相较于部分经典的元启发式算法和典型的新型群智能算法,系统成本减少20.79%~65.39%,所提任务卸载算法相对于本地计算策略的平均系统成本能够降低66.98%。

Mobile Edge Computing and Field Trial Results for 5G Low Latency Scenario

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.

Recent advances in mobile edge computing and content caching

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.

无人机辅助MEC系统中面向用户公平性的三维部署和卸载优化

针对无人机辅助移动边缘计算系统存在的用户公平性不足问题,本文提出了一种面向用户公平性的三维部署和卸载优化算法.该算法综合考虑用户匹配、无人机三维部署、计算资源分配、卸载因子对系统总时延及用户公平性的影响,建立了一个最小化系统总时延的多元优化问题,并针对该问题提出了一种两阶段联合优化算法,其中第1阶段使用带有平衡约束的聚类算法解决用户匹配和无人机的水平部署问题,第2阶段使用凸优化算法迭代求解无人机高度部署,资源分配和卸载因子优化问题.实验结果表明,与4种基准算法相比,所提算法在系统总时延和用户公平性两方面具有更好的性能.

Reliable and Energy-Aware Job Offloading at Terahertz Frequencies for Mobile Edge Computing

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.

Novel Private Data Access Control Scheme Suitable for Mobile Edge Computing

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.

On the System Performance of Mobile Edge Computing in an Uplink NOMA WSN With a Multiantenna Access Point Over Nakagami-m Fading

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.

基于深度强化学习的IRS辅助NOMA-MEC通信资源分配优化

为了解决无法与边缘服务器建立直连通信链路的盲区边缘用户卸载任务的问题,设计了一个基于深度强化学习(deep reinforcement learning, DRL)的智能反射面(intelligent reflecting surface, IRS)辅助非正交多址(non-orthogonal multiple access, NOMA)通信的资源分配优化算法,以获得由系统和速率和能源效率(energy efficiency, EE)加权的最大系统收益,从而实现绿色高效通信。通过深度确定性策略梯度(deep deterministic policy gradient, DDPG)算法联合优化传输功率分配和IRS的反射相移矩阵。仿真结果表明,使用DDPG算法处理移动边缘计算(mobile edge computing, MEC)的通信资源分配优于其他几种对比实验算法。

面向智能电网的融合通信MEC卸载策略

随着智能电网系统中移动终端的增加,其对传输数据低时延、大带宽和高可靠性的需求尤为紧迫。为解决其中无线传输、信息处理和可靠性不足等问题,文章采用“切片分组网(sliced packet network,SPN)+可信无线局域网(wireless local area network,WLAN)”通信新技术网络架构,建立多种移动终端设备安全无线传输和计算任务卸载的总时延优化卸载模型,提出一种基于交替优化技术的算法。仿真结果表明,该策略不仅保证设备安全高效地接入网络,还可显著降低传输时延,具有优异的成本效益。