Method of ciphertext retrieval in mobile edge computing based on block segmentation

Aiming at the efficiency of cloud computing ciphertext retrieval scheme,a method of ciphertext retrieval in mobile edge computing based on block segmentations was proposed.Firstly,the edge server was introduced to calculate the document similarity score,thereby the computational cost of cloud server was reduced and the processing efficiency of cloud server was improved.Secondly,most keywords that are not related to the query were filtered out by a method of block segmentations based on the MRSE scheme,thereby the efficiency of calculating the document similarity score was improved.Theoretical analysis and experimental results show that the solution is safe under the known background threat model.Compared with the existing scheme,the proposed scheme has the same security and higher retrieval efficiency.     

移动边缘计算发展与应用研究

对MEC的概念、体系架构、产业发展方向等内容进行了介绍,讨论了云边协同一些典型的应用场景,并对MEC技术发展存在的问题和未来的推进策略进行了阐述。     

Research on intelligent computing offloading model based on reputation value in mobile edge computing

Aiming at the problem of high-latency,high-energy-consumption,and low-reliability mobile caused by computing-intensive and delay-sensitive emerging mobile applications in the explosive growth of IoT smart mobile terminals in the mobile edge computing environment,an offload decision-making model where delay and energy consumption were comprehensively included,and a computing resource game allocation model based on reputation that took into account was proposed,then improved particle swarm algorithm and the method of Lagrange multipliers were used respectively to solve models.Simulation results show that the proposed method can meet the service requirements of emerging intelligent applications for low latency,low energy consumption and high reliability,and effectively implement the overall optimized allocation of computing offload resources.     

PSMECS: A provably secure ID-based communication in mobile edge computing

Human-centered systems play an important role in the modern world, for example, driverless car, autonomous and smart vehicles, drones, and robotics. The internet of things environment demands a faster real-time response depending on the applications processed in a particular duration. Mobile edge computing (MEC) allows a user to get a real-time response as compared with cloud computing (CC), although ensuring a number of security attributes in MEC environment remains challenging. In this article, a protocol is designed to achieve mutual authentication, anonymous communication, and security against traceability, as these are very crucial factors to ensure the security of data and user's privacy. Moreover, the proposed scheme ensures mutual authentication between a mobile user and an edge server along with the user's anonymity and untraceability. The proof of security and evaluation of performance of the scheme validates that it ensures security attributes and improves efficiency in terms of communication and computation overheads.     

移动边缘计算卸载技术综述

移动边缘计算(Mobile Edge Computing,MEC)将云服务器的计算资源扩展到更靠近用户一侧的网络边缘,使得用户可以将任务卸载到边缘服务器,从而克服原先云计算中将任务卸载到云服务器所带来的高时延问题。首先介绍了移动边缘计算的基本概念、基本框架和应用场景,然后围绕卸载决策、联合资源分配的卸载决策分别从单MEC服务器和多MEC服务器两种场景总结了任务卸载技术的研究现状,最后结合当前MEC卸载技术中存在的不足展望了未来MEC卸载技术的研究。     

Proactive content caching by exploiting transfer learning for mobile edge computing

To address the vast multimedia traffic volume and requirements of user quality of experience in the next‐generation mobile communication system (5G), it is imperative to develop efficient content caching strategy at mobile network edges, which is deemed as a key technique for 5G. Recent advances in edge/cloud computing and machine learning facilitate efficient content caching for 5G, where mobile edge computing can be exploited to reduce service latency by equipping computation and storage capacity at the edge network. In this paper, we propose a proactive caching mechanism named learning‐based cooperative caching (LECC) strategy based on mobile edge computing architecture to reduce transmission cost while improving user quality of experience for future mobile networks. In LECC, we exploit a transfer learning‐based approach for estimating content popularity and then formulate the proactive caching optimization model. As the optimization problem is NP‐hard, we resort to a greedy algorithm for solving the cache content placement problem. Performance evaluation reveals that LECC can apparently improve content cache hit rate and decrease content delivery latency and transmission cost in comparison with known existing caching strategies.     

大规模无线通信网络移动边缘计算和缓存研究

面向未来6G移动通信的大规模网络移动边缘计算与缓存技术,首先,介绍了大规模无线网络下移动边缘计算和缓存的架构与原理,并阐释了移动边缘计算和缓存技术在大规模无线网络中的必要性和普适性.接着,从计算卸载、边缘缓存、多维资源分配、用户关联和隐私保护这5个关键问题出发,综述和分析了移动边缘计算和缓存赋能大规模无线网络时会引入的...     

基于边缘计算技术的智能无人系统

智能无人系统是信息化战争中夺取信息优势、实施精确打击、完成特殊作战任务的重要手段之一,是未来军事力量的倍增器。如何应用新兴技术提升智能无人系统在未来作战场景的有效性是当前重要的研究方向之一。介绍了智能无人系统的相关背景,引入边缘计算技术以及其应用在智能无人系统中的优势,对边缘计算技术在智能无人系统中的问题进行建模及算法求解,最后通过仿真实验验证了算法的有效性。     

任务迁移的移动边缘计算系统中资源分配和任务卸载策略

针对在任务卸载时由于设备的移动而导致任务迁移这一问题,将任务卸载过程建模为马尔科夫决策过程,并通过优化资源分配和任务卸载策略,解决基于联合时延和能耗的损耗函数最小的优化问题。首先将问题转化为最小化损耗函数之和,并在决策前对每个任务的传输功率采用二分法进行优化,然后基于获得的传输功率提出一种QLBA（Q-learning Based Algorithm）来完成卸载决策。仿真结果证实所提方案优于传统算法。     

Optimal dynamic spectrum allocation-assisted latency minimization for multiuser mobile edge computing

Mobile Edge Computing (MEC) has been envisioned as an efficient solution to provide computation-intensive yet latency-sensitive services for wireless devices. In this paper, we investigate the optimal dynamic spectrum allocation-assisted multiuser computation offloading in MEC for overall latency minimization. Specifically, we first focus on a static multiuser computation offloading scenario and jointly optimize users' offloading decisions, transmission durations, and Edge Servers' (ESs) resource allocations. Owing to the nonconvexity of our joint optimization problem, we identify its layered structure and decompose it into two problems: a subproblem and a top problem. For the subproblem, we propose a bisection search-based algorithm to efficiently find the optimal users' offloading decisions and ESs’ resource allocations under a given transmission duration. Second, we use a linear search-based algorithm for solving the top problem to obtain the optimal transmission duration based on the result of the subproblem. Further, after solving the static scenario, we consider a dynamic scenario of multiuser computation offloading with time-varying channels and workload. To efficiently address this dynamic scenario, we propose a deep reinforcement learning-based online algorithm to determine the near-optimal transmission duration in a real-time manner. Numerical results are provided to validate our proposed algorithms for minimizing the overall latency in both static and dynamic offloading scenarios. We also demonstrate the advantages of our proposed algorithms compared to the conventional multiuser computation offloading schemes.     

A blockchain-enabled security management framework for mobile edge computing

Mobile edge computing (MEC) integrates mobile and edge computing technologies to provide efficient computing services with low latency. It includes several Internet of Things (IoT) and edge devices that process the user data at the network's edge. The architectural characteristic of MEC supports many internet-based services, which attract more number of users, including attackers. The safety and privacy of the MEC environment, especially user information is a significant concern. A lightweight accessing and sharing protocol is required because edge devices are resource constraints. This paper addresses this issue by proposing a blockchain-enabled security management framework for MEC environments. This approach provides another level of security and includes blockchain security features like temper resistance, immutable, transparent, traceable, and distributed ledger in the MEC environment. The framework guarantees secure data storage in the MEC environment. The contributions of this paper are twofold: (1) We propose a blockchain-enabled security management framework for MEC environments that address the security and privacy concerns, and (2) we demonstrate through simulations that the framework has high performance and is suitable for resource-constrained MEC devices. In addition, a smart contract-based access and sharing mechanism is proposed. Our research uses a combination of theoretical analysis and simulation experiments to demonstrate that the proposed framework offers high security, low latency, legitimate access, high throughput, and low operations cost.     

车联网场景下移动边缘计算协作式资源分配策略

为了有效利用边缘云的计算资源,尽可能降低任务卸载时的平均等待时延,提出了一种满足边缘计算服务器容限阈值和任务卸载成功率约束条件下的多个边缘计算服务器相互协作的资源分配方案,通过单位时间总代价指标优化边缘计算服务器个数.将此方案建模为一个整数优化问题,之后设计了一种最小代价算法求解此优化问题,得到约束条件下的单位时间总代...     

基于边缘计算的数据密集型服务部署

日益增长的数据量对数据处理的要求越来越高,于是出现了数据密集型服务。在解决复杂问题时,多个数据密集型服务通常会形成一个服务组合。由于服务组件之间存在大量的数据传输,巨大的传输时延会对系统的整体性能造成影响。在边缘计算环境中,基于否定选择算法,为降低服务组合中的数据传输时间提出了一种优化部署策略。首先,给出了此类数据密集型服务组件部署问题的定义,并为该部署问题构建优化模型;然后,设计了一种否定选择算法来获取最佳的部署方案;为了评估该算法的适用性和收敛性,使用遗传算法和模拟退火算法与其对比,结果显示,提出的算法在这种数据密集型服务组件的部署问题中表现得更为出色。     

Service migration in mobile edge computing: A deep reinforcement learning approach

In mobile edge computing, service migration can not only reduce the access latency but also reduce the network costs for users. However, due to bandwidth bottleneck, migration costs should also be considered during service migration. In this way, the trade-off between benefits of service migration and total service costs is very important for the cloud service providers. In this paper, we propose an efficient dynamic service migration algorithm named SMDQN, which is based on reinforcement learning. We consider each mobile application service can be hosted on one or more edge nodes and each edge node has limited resources. SMDQN takes total delay and migration costs into consideration. And to reduce the size of Markov decision process space, we devise the deep reinforcement learning algorithm to make a fast decision. We implement the algorithm and test the performance and stability of it. The simulation result shows that it can minimize the service costs and adapt well to different mobile access patterns.     

Service function chain embedding algorithm with wireless multicast in mobile edge computing network

To resolve the excessive system overhead and serious traffic congestion in user-oriented service function chain (SFC) embedding in mobile edge computing (MEC) networks,a content-oriented joint wireless multicast and SFC embedding algorithm was proposed for the multi-base station and multi-user edge networks with MEC servers.By involving four kinds of system overhead,including service flow,server function sustaining power,server function service power and wireless transmission power,an optimization model was proposed to jointly design SFC embedding with multicast beamforming.Firstly,with Lagrangian dual decomposition,the problem was decoupled into two independent subproblems,namely,SFC embedding and multicast beamforming.Secondly,with the L_p norm penalty term-based successive convex approximation algorithm,the integer programming-based SFC embedding problem was relaxed to an equivalent linear programming one.Finally,the non-convex beamforming optimization problem was transformed into a series of convex ones via the path following technique.Simulation results revealed that the proposed algorithm has good convergence,and is superior to both the optimal SFC embedding with unicasting and random SFC embedding with multicasting in terms of system overhead.     

Intelligent file transfer for smart handheld devices based on mobile cloud computing

Most methods of file transfer between mobile devices have to take considerable time to obtain and input target identifiers. To speed up file sharing of mobile devices, an intelligent file transfer framework is designed based on mobile cloud computing. Only single‐finger action is performed in the proposed framework, which just drags the file to the target face on the touchscreen of a mobile device. The dragged file can be transmitted to the target receiver who can either get the file immediately as her/his mobile device is online or receive the file later after connecting to the Internet. Our framework provides the following features: (i) users do not need to know the target identifier in advance; (ii) users do not need to input identity information by themselves; and (iii) users can select a specific target among multiple candidates from the camera of a mobile device. The intelligent file transfer framework reveals an efficient architecture and innovative user interfaces to transfer files between mobile devices, which can significantly reduce the complexity and difficulty of file sharing. An Android‐based prototype is implemented to verify the feasibility and superiority of our framework. Experimental results show that our approach outperforms existing schemes and can save large amounts of time in file sharing for mobile users. Copyright © 2015 John Wiley & Sons, Ltd.     

虚拟现实技术的应用现状及发展

介绍了虚拟现实技术的构成和技术特点，回顾了虚拟现实技术的发展历史和研究背景，分析了虚拟现实系统的应用现状，重点论述了分布式虚拟现实系统的应用和发展前景。     

Computation energy efficiency maximization based resource allocation scheme in wireless powered mobile edge computing network

For wireless powered mobile edge computing (MEC) network,a system computation energy efficiency (CEE) maximization scheme by considering the limited computation capacity at the MEC server side was proposed.Specifically,a CEE maximization optimization problem was formulated by jointly optimizing the computing frequencies and execution time of the MEC server and the edge user(EU),the transmit power and offloading time of each EU,the energy harvesting time and the transmit power of the power beacon.Since the formulated optimization problem was a non-convex fractional optimization problem and hard to solve,the formulated problem was firstly transformed into a non-convex subtraction problem by means of the generalized fractional programming theory and then transform the subtraction problem into an equivalent convex problem by introducing a series of auxiliary variables.On this basis,an iterative algorithm to obtain the optimal solutions was proposed.Simulation results verify the fast convergence of the proposed algorithm and show that the proposed resource allocation scheme can achieve a higher CEE by comparing with other schemes.