关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) has been envisaged as one of the most promising technologies in the fifth generation (5G) mobile networks. It allows mobile devices to offload their computation-demanding and latency-critical tasks to the resource-rich MEC servers. Accordingly, MEC can significantly improve the latency performance and reduce energy consumption for mobile devices. Nonetheless, privacy leakage may occur during the task offloading process. Most existing works ignored these issues or just investigated the system-level solution for MEC. Privacy-aware and user-level task offloading optimization problems receive much less attention. In order to tackle these challenges, a privacy-preserving and device-managed task offloading scheme is proposed in this paper for MEC. This scheme can achieve near-optimal latency and energy performance while protecting the location privacy and usage pattern privacy of users. Firstly, we formulate the joint optimization problem of task offloading and privacy preservation as a semiparametric contextual multi-armed bandit (MAB) problem, which has a relaxed reward model. Then, we propose a privacy-aware online task offloading (PAOTO) algorithm based on the transformed Thompson sampling (TS) architecture, through which we can (1) receive the best possible delay and energy consumption performance, (2) achieve the goal of preserving privacy, and (3) obtain an online device-managed task offloading policy without requiring any system-level information. Simulation results demonstrate that the proposed scheme outperforms the existing methods in terms of minimizing the system cost and preserving the privacy of users.

关键词： Mobile edge computing  

摘要： As a key technology in the 5G era, Mobile Edge Computing (MEC) has developed rapidly in recent years. MEC aims to reduce the service delay of mobile users, while alleviating the processing pressure on the core network. MEC can be regarded as an extension of cloud computing on the user side, which can deploy edge servers and bring computing resources closer to mobile users, and provide more efficient interactions. However, due to the user's dynamic mobility, the distance between the user and the edge server will change dynamically, which may cause fluctuations in Quality of Service (QoS). Therefore, when a mobile user moves in the MEC environment, certain approaches are needed to schedule services deployed on the edge server to ensure the user experience. In this paper, we model service scheduling in MEC scenarios and propose a delay-aware and mobility-aware service management approach based on concise probabilistic methods. This approach has low computational complexity and can effectively reduce service delay and migration costs. Furthermore, we conduct experiments by utilizing multiple realistic datasets and use iFogSim to evaluate the performance of the algorithm. The results show that our proposed approach can optimize the performance on service delay, with 8% to 20% improvement and reduce the migration cost by more than 75% compared with baselines during the rush hours. © 2021, CC0.

关键词： Mobile edge computing  

摘要： Space information networks (SIN) are facing an ever-increasing thirst for high-speed and high-capacity seamless data transmission due to the integration of ground, air, and space communications. However, this imposes a new paradigm on the architecture design of the integrated SIN. Recently, reconfigurable intelligent surfaces (RISs) and mobile edge computing (MEC) are the most promising techniques, conceived to improve communication and computation capability by reconfiguring the wireless propagation environment and offloading. Hence, converging RISs and MEC in SIN is becoming an effort to reap the double benefits of computation and communication. In this article, we propose an RIS-assisted collaborative MEC architecture for SIN and discuss its implementation. Then we present its potential benefits, major challenges, and feasible applications. Subsequently, we study different cases to evaluate the system data rate and latency. Finally, we conclude with a list of open issues in this research area. Copyright © 2021, The Authors. All rights reserved.

关键词： mobile edge computing   multi-UAV   computation efficiency   secure transmission   jamming   trajectory scheduling  

摘要： In this study, we consider a security efficiency maximization problem in a multiple unmanned aerial vehicle (UAV)-aided system with mobile edge computing (MEC). Two kinds of UAVs, including multiple computing UAVs (CUAVs) and multiple jamming UAVs (JUAVs), are considered in this system. CUAVs would receive partial computation bits and send the computation results to ground users. JUAVs do not undertake computing tasks and only send interference signals to counter potential ground eavesdroppers. We jointly optimize the ground user scheduling, UAV power, and UAV trajectory to maximize the security efficiency. The original problem is non-convex and difficult to solve. We first use the Dinkelbach method combined with continuous convex approximation technology, and then propose three corresponding subproblems, including user scheduling subproblem, UAV power subproblem, and UAV trajectory problem. Further, we apply the branch and bound method to solve the user scheduling subproblem, and optimize the two remaining subproblems by introducing auxiliary variables and Taylor expansion. The simulation results show that the proposed scheme can obtain better secure off-loading efficiency with respect to the existing schemes.

关键词： Mobile edge computing   spatial crowdsourcing   task allocation   mobile networks  

摘要： With the progress of mobile devices and the successful forms of using the wisdom of crowds, spatial crowdsourcing has attracted much attention from the research community. The idea of spatial crowdsourcing is recruiting a set of available crowds to finish the spatial tasks located in crowdsourcing locations, e.g., landmarks, by using their handheld devices. This paper addresses the worker recruitment problem in spatial crowdsourcing under the coverage and workload-balancing requirements. The coverage constraint means that any crowdsourcing location should be visited by at least one of the recruited workers to satisfy the Quality-of-Service requirement, e.g., traffic monitoring or climate forecast. In addition, we argue that each crowdsourcing operation has a cost in reality, e.g., data traffic or energy consumption and the resource may be limited at each crowdsourcing location. The objective of this paper is to solve a Coverage and Balanced Crowdsourcing Recruiting (CBCR) problem, which ensures the coverage requirement and minimizes the maximum crowdsourcing cost for any crowdsourcing location. We prove that the CBCR problem is NP-hard in the general case. Then, we discuss the CBCR problem in the 1-D scenario. In the 1-D scenario, we first propose a directionally coverage scheme and further extend it to a Polynomial-Time Approximation Scheme (PTAS) to trade-off the computation complexity and the performance. The performance can be bounded to 2 + epsilon, where epsilon can be an arbitrary small value. Then, we found that there exists a sub-optimal structure, and thus the dynamic programming approach is proposed to find the optimal solution in the 1-D scenario. In the general 2-D scenario, we first prove that it has a sub-modular property and thus the naive greedy algorithm has an approximation ratio of ln n + 1. In addition, we propose a randomized rounding algorithm with an expectation bound of O(log n/log log n). Extensive experiments on realistic traces demonstrat

关键词： Machine learning   Internet of Things   traffic steering   mobile edge computing  

摘要： In the Next Generation Radio Networks(NGRN),there will be extreme massive connectivity with the Heterogeneous Internet of Things(HetIoT)*** millimeter-Wave(mmWave)communications will become a potential core technology to increase the capacity of Radio Networks(RN)and enable Multiple-Input and Multiple-Output(MIMO)of Radio Remote Head(RRH)***,the challenging key issues in unfair radio resource handling remain unsolved when massive requests are occurring *** imbalance of resource utilization is one of the main issues occurs when there is overloaded connectivity to the closest RRH receiving exceeding *** handle this issue effectively,Machine Learning(ML)algorithm plays an important role to tackle the requests of massive IoT devices to RRH with its obvious capacity *** paper proposed a dynamic RRH gateways steering based on a lightweight supervised learning algorithm,namely K-Nearest Neighbor(KNN),to improve the communication Quality of Service(QoS)in real-time IoT *** supervises the model to classify and recommend the user’s requests to optimal RRHs which preserves higher *** experimental dataset was generated by using computer software and the simulation results illustrated a remarkable outperformance of the proposed scheme over the conventional methods in terms of multiple significant QoS parameters,including communication reliability,latency,and throughput.

关键词： DASH   mobile edge computing   multicast   quality of experience   adaptive bitrate algorithm  

摘要： Dynamic adaptive streaming over HTTP(DASH)can adaptively select the appropriate video bitrate for mobile *** edge computing(MEC)scenario is of great benefit to improve the performance of mobile networks by providing computing and storage *** the utilization of spectrum resources can be improved by multicast transmission,but the performance of the multicast transmission will be directly affected by the selected grouping algorithm and resource allocation *** order to improve the quality of experience(QoE)of video users in the 5G MEC scenario,this paper proposes a QoE-driven DASH multicast scheme,which mainly covers the grouping algorithm and the adaptive bitrate(ABR)*** of all,we take the optimized target QoE as the basis for grouping and propose an adaptive grouping algorithm that can dynamically adjust the grouping ***,we design a joint optimization ABR algorithm based on the prediction of QoE,which comprehensively considers the process of resource allocation and bitrate decision-making based on the prediction of QoE of video segments in a certain forward-looking field of *** simulation results show that the proposed DASH multicast scheme performs well in QoE and fairness.

关键词： Mobile edge computing  

摘要： Driven by the recent technological advances and the flourishing innovative applications, mobile edge computing (MEC) has emerged as a promising computing paradigm, aiming to extend cloud computing services from the centralized cloud to the edges of networks. Indeed, it is an essential component in the fifth generation architecture, which can empower users with rapid and powerful computation, cache capacity, energy efficiency, mobility, location, and context awareness support. However, as network access methods become diverse, how to ensure ultra-reliable and low-latency communications in MEC is a very challenging task. This article tries to present a comprehensive survey of relevant research and technological developments in MEC. We first highlight the advantages of MEC in comparison with mobile cloud computing, based on which we then make a holistic overview of MEC, including its related architectures and key enablers. Subsequently, we introduce several specific themes and the existing techniques in MEC, where we are particularly concerned with efficient communication techniques and reliable mechanisms in MEC. Furthermore, we elaborate on the evolution of MEC standardization and discuss several typical application scenarios. Finally, we strive to shed light on some challenges and potential research directions for MEC, which may facilitate the transformation of MEC from theory to practice. © 2017 IEEE.

关键词： Mobile edge computing   Computation offloading   Context-aware   Federated learning  

摘要： Mobile edge computing (MEC) provides an effective solution to help the Internet of Things (IoT) devices with delay-sensitive and computation-intensive tasks by offering computing capabilities in the proximity of mobile device users. Most of the existing studies ignore context information of the application, requests, sensors, resources, and network. However, in practice, context information has a significant impact on offloading decisions. In this paper, we consider context-aware offloading in MEC with multi-user. The contexts are collected using autonomous management as the MAPE loop in all offloading processes. Also, federated learning (FL)-based offloading is presented. Our learning method in mobile devices (MDs) is deep reinforcement learning (DRL). FL helps us to use distributed capabilities of MEC with updated weights between MDs and edge devices (Eds). The simulation results indicate our method is superior to local computing, offload, and FL without considering context-aware algorithms in terms of energy consumption, execution cost, network usage, delay, and fairness.

关键词： Mobile edge computing   Latency   Throughput   Traffic decentralization   Mininet  

摘要： In the centralized architecture of traditional mobile cellular network, every traffic of mobile terminals has to traverse the centered network core and then deliver to the corresponding service mobile terminals. Since the traffic load is concentrated on the central network core, it is difficult to deliver the explosive amount of network traffic and guarantee latency requirements of diversifying applications. In order to solve this problem, the mobile edge computing (MEC) architecture that distributes the traffic load by locating the computing and caching server closer to the terminal is currently being developed. In this paper, the effect of distribution of mobile traffic with MEC is evaluated in terms of throughput enhancements and latency reduction. The simulations were conducted using Mininet to determine what performance gains can be obtained when applying the MEC architecture to the mobile cellular network. As a result, under the same link bandwidth condition, MEC architecture provides an increase in data rates of approximately 265% for high-quality video transmission and about 162% for low-quality video transmission, and shows the reduced delay of packet arrival times. (C) 2021 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V.