关键词： Task analysis   Array signal processing   Wireless communication   Downlink   Uplink   Program processors   Optimization   Wireless power transfer   intelligent reflecting surface   mobile edge computing  

摘要： This letter studies the joint active and passive beamforming in intelligent reflecting surface (IRS)-enhanced wireless powered mobile edge computing (MEC) networks, in which a hybrid access point (HAP) is deployed to charge multiple wireless devices (WDs) and assist in computation of the offloaded tasks from the WDs. An IRS is deployed to enhance the downlink wireless power transfer (WPT) and the uplink tasks offloading (TO). To maximize the systems' sum computation rate, an optimization problem is formulated by jointly optimizing the time assignment of WPT and TO, the active beamforming at the HAP, the transmit power and computing frequency of the WDs, and the passive beamforming at the IRS. To tackle the non-convex problem, we present an efficient algorithm by utilizing the alternating optimization (AO) and semidefinite relaxation (SDR) techniques. Simulation results show that by deploying IRS in the system, a significant computation rate gain can be achieved. Especially, when the IRS is closer to the WDs or the HAP, the much higher computation rate can be reached.

关键词： Games   Servers   Delays   Wireless communication   Rendering (computer graphics)   Approximation algorithms   Virtual reality   Mobile edge computing   resource allocation   virtual reality   multiplayer interactive game  

摘要： Wireless multiplayer interactive virtual reality (VR) game has the high computing workload of VR and unpredictable interaction among players, which brings severe challenges to the design of wireless communication systems. In this paper, we propose a wireless multiplayer interactive VR game transmission framework based on mobile edge computing (MEC) that is able to model the interaction among players and compute the post-processing procedures at the MEC server or the mobile VR device. In the framework, the absolute delay of each player is used to avoid VR vertigo and the inter-player delay among players is used to model the fairness of the interactive game. Aiming to minimize the average inter-player delay, we optimize the computing resource allocation of the MEC server, the wireless bandwidth allocation and the post-processing decision policy subject to the constraints of the absolute delay requirements, the local energy limits of players, the total bandwidth limit and the computing resources limit. To tackle the non-convex problem efficiently, we design an iterative algorithm based on the NESTT-G algorithm which iteratively optimizes the truncated first-order Taylor approximation of the objective. Numerical results demonstrate the proposed algorithm can reduce the average inter-player delay significantly with lower complexity, and also reveal the impact of different parameters and the channel state conditions on the post-processing decision and edge resource allocation.

关键词： cooperative offloading   decision-making   intelligent computing   mobile edge computing   resource allocation  

摘要： The multiuser cooperative offloading mobile edge computing (COMEC) system has attracted much attention because it can realize delay-sensitive tasks. However, in the coupling optimization of offloading decision and resource allocation, the existing numerical optimization algorithms are difficult to obtain high-quality optimization solutions. In this paper, we propose a differential dynamic gradient descent (DDGD) optimization algorithm to solve the above optimization problems. DDGD algorithm decomposes the constrained NP-hard optimization problem into two network layers and integrates the constraint function into a larger end-to-end training network. These two-layer networks encode the dependencies and optimization constraints between parameter hidden states, which cannot be captured by a numerical optimization model or a full connection layer neural network. Because the ring learning and self-repeating learning architecture are adopted and the information is stored in the differential dynamics network, the proposed algorithm can achieve better and more intelligent decision-making in searching the solution trajectory without setting accurate parameters in advance and reduce the complexity of the network. We show that compared with the baseline method, the DDGD method has superior optimization performance in the energy consumption optimization of COMEC.

关键词： Unmanned aerial vehicle   mobile edge computing   blockchain   security   computation offloading  

摘要： The introduction of mobile edge computing (MEC) technology in unmanned aerial vehicle (UAV) networks can provide computing services for mobile users with or without communication infrastructure coverage. However, mobile users' privacy may be leaked during the computation offloading process due to the information interaction between UAVs and the migration of computation tasks between mobile users and UAVs. To this end, we propose a secure aerial computing architecture that integrates MEC and blockchain technology for UAV networks to effectively ensure the security and privacy of computation offloading between UAVs and mobile users. Under this architecture, a problem of joint optimization of user association, UAV trajectory, block processor scheduling, and computation resource allocation is formulated to minimize the weighted sum of the energy consumption and the delay in completing computation tasks and blockchain tasks processing. To handle this intractable issue, we first decouple the optimization variables and then separate the original problem into multiple subproblems to be solved alternately. In addition, we design a block coordinate descent (BCD)-based algorithm for user association and computation resource allocation, and a successive convex approximation (SCA)-based algorithm to optimize the trajectories of UAVs. Simulation results show that the proposed algorithm has better performance.

关键词： mobile edge computing   task offloading   delay sensitive   Markov decision process   deep reinforcement learning  

摘要： With the internet developing rapidly, mobile edge computing (MEC) has been proposed to offer computational capabilities to tackle the high latency caused by innumerable data and applications. Due to limited computing resources, the innovation of computation offloading technology for an MEC system remains challenging, and can lead to transmission delays and energy consumption. This paper focuses on a task-offloading scheme for an MEC-based system where each mobile device is an independent agent and responsible for making a schedule based on delay-sensitive tasks. Nevertheless, the time-varying network dynamics and the heterogeneous features of real-time data tasks make it difficult to find an optimal solution for task offloading. Existing centralized-based or distributed-based algorithms require huge computational resources for complex problems. To address the above problem, we design a novel deep reinforcement learning (DRL)-based approach by using a parameterized indexed value function for value estimation. Additionally, the task-offloading problem is simulated as a Markov decision process (MDP) and our aim is to reduce the total delay of data processing. Experimental results have shown that our algorithm significantly promotes the users' offloading performance over traditional methods.

关键词： Mobile edge computing   software decomposition   federated learning   distributed computing  

摘要： Deep Learning (DL) is an essential technology formodern intelligent sensor network and interactive multimedia applications, having problems with user data privacy when training on a central cloud. While Federated Learning (FL) motivates to preserve user privacy, it also causes new problems of lower user terminal usability and training efficiency, which caused substantial energy consumption. This article proposes a novel energy-efficient and privacy-aware decomposition framework to improve user-side FL efficiency under pre-defined privacy requirements with the assistance of Mobile Edge Computing (MEC) and Software Decomposition. It takes the propagation of each neural layer as the migrating unit and considers the tradeoff relationship between privacy and efficiency. We also propose an online scheduling algorithm to optimize the framework's training performance. Furthermore, we summarize eight privacy-sensitive information classes onwhich existing privacy attacks base and design configurable privacy preservation mechanisms for each class. Simulations and experiments prove the effectiveness of our framework and algorithm in FL efficiency improvement and the effects of different privacy constraints on the overall training efficiency.

关键词： Mobile edge computing   parked vehicle   reliable service   task offloading   resource allocation  

摘要： Nowadays, a growing number of computation-intensive applications appear in our daily life. Those applications make the loads of both the core network and the mobile devices, in terms of energy and bandwidth, hugely increase. Offloading computation-intensive tasks to edge cloud is proposed to address this issue. Since edge clouds have limited computation resources compared with the remote cloud, they would get over-loaded because of the heavy computation burden. Parked-vehicle-assisted mobile edge computing becomes one of the promising solutions for this problem. However, several critical issues in parked-vehicle-assisted mobile edge computing would result in low reliable edge service. The open environment would bring about uncertainty, and the data privacy is hard to ensure. In addition, different from edge cloud, each parked vehicle only has limited parking duration and can leave unexpectedly for personal reasons. Moreover, edge cloud and vehicle adopt different execution models of computation and communication. The heterogeneous environment may result in negative effect on cooperativeness. Ignoring those issues can result in substantial performance degradation. To tackle this challenge and explore the benefits of parked-vehicle-assisted offloading, we study the task offloading and resource-allocation problem by fully considering the above issues. First, we propose a resource-management scheme to address the privacy issue. Second, we review the execution model of computation and communication in parked-vehicle-assisted computation offloading. Then, we formulate the problem into a mixed-integer nonlinear programming. The problem is hard to tackle due to its non-convex nature, which means that the time complexity of finding global optimal solution is unaffordable. Finally, we decompose the original problem into two sub-problems with lower complexity, and related algorithms are given to deal with the sub-problems. Simulation results demonstrate the effectiveness of th

关键词： Big data   IoT   Mobile edge computing   Deep learning   Feature selection  

摘要： In present times, a massive quantity of big data has been generated by the Internet of Things (IoT) devices for a wide range of applications. The IoT devices generate an enormous data quantity that is troublesome for data processing and analytics functionalities, which is effortlessly managed by the cloud before the explosive development of the IoT. Specifically, the big IoT data analytics by mobile edge computing (MEC) becomes a hot research topic and needs comprehensive research works for intelligent decision making. This paper introduces a new generative adversarial network (GAN) with a quantum elephant herd optimization (QEHO) algorithm for MEC in IoT enabled big data environment called GAN-QEHO. The presented GAN-QEHO algorithm follows two-stage processes, namely feature selection (FS) and data classification. The QEHO algorithm is used to elect an optimal feature subset for the FS process. By the quantization of elephant individuals, the search scope of feature space can be enhanced, and an optimal tradeoff has been attained among exploration and exploitation. Then, the GAN model is employed for the classification process to identify different class labels. In order to validate the experimental results analysis of the GAN-QEHO algorithm, a series of simulations take place in terms of diverse aspects.

关键词： Deep reinforcement learning   Integrated trust mechanism   Mobile edge computing   Task offloading  

摘要： As a new computing model, mobile edge computing (MEC) is designed to better deal with various forms of service requests, such as computing intensity and delay sensitivity, in the era of big data and the Internet of Things (IoT). However, the development of MEC is still in its infancy, and many issues need to be further investigated. One of the key issues that needs to be addressed in MEC is rational task offloading. Due to the dynamic, real time and complex nature of the MEC environment, the security and reliability of edge data are becoming increasingly important. Based on the above problems, we construct a task offloading integrated trust evaluation mechanism and, combined with the double deep Q-network (DDQN) algorithm in deep reinforcement learning (DRL), propose a novel task offloading algorithm, named DDTMOA. Simulation results show that the DDTMOA algorithm can effectively reduce the average task response time and total system energy consumption while ensuring task offloading performance compared to other classical algorithms. (C) 2022 Elsevier Inc. All rights reserved.

关键词： mobile edge computing   intelligent reflecting surfaces   latency   optimization  

摘要： Intelligent reflecting surfaces (IRS) and mobile edge computing (MEC) have recently attracted significant attention in academia and industry. Without consuming any external energy, IRS can extend wireless coverage by smartly reconfiguring the phase shift of a signal towards the receiver with the help of passive elements. On the other hand, MEC has the ability to reduce latency by providing extensive computational facilities to users. This paper proposes a new optimization scheme for IRS-enhanced mobile edge computing to minimize the maximum computational time of the end users' tasks. The optimization problem is formulated to simultaneously optimize the task segmentation and transmission power of users, phase shift design of IRS, and computational resource of mobile edge. The optimization problem is non-convex and coupled on multiple variables which make it very complex. Therefore, we transform it to convex by decoupling it into sub-problems and then obtain an efficient solution. In particular, the closed-form solutions for task segmentation and edge computational resources are achieved through the monotonical relation of time and Karush-Kuhn-Tucker conditions, while the transmission power of users and phase shift design of IRS are computed using the convex optimization technique. The proposed IRS-enhanced optimization scheme is compared with edge computing nave offloading, binary offloading, and edge computing, respectively. Numerical results demonstrate the benefits of the proposed scheme compared to other benchmark schemes.