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.     

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.     

Survey on data security and privacy-preserving for the research of edge computing

With the rapid development and extensive application of the Internet of things (IoT),big data and 5G network architecture,the massive data generated by the edge equipment of the network and the real-time service requirements are far beyond the capacity if the traditional cloud computing.To solve such dilemma,the edge computing which deploys the cloud services in the edge network has envisioned to be the dominant cloud service paradigm in the era of IoT.Meanwhile,the unique features of edge computing,such as content perception,real-time computing,parallel processing and etc.,has also introduced new security problems especially the data security and privacy issues.Firstly,the background and challenges of data security and privacy-preserving in edge computing were described,and then the research architecture of data security and privacy-preserving was presented.Secondly,the key technologies of data security,access control,identity authentication and privacy-preserving were summarized.Thirdly,the recent research advancements on the data security and privacy issues that may be applied to edge computing were described in detail.Finally,some potential research points of edge computing data security and privacy-preserving were given,and the direction of future research work was pointed out.     

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...     

移动云服务的数据安全与隐私保护综述

移动云服务相比传统云具有移动互联、灵活终端应用和便捷数据存取等特点。然而,丰富的移动云服务应用也带来了更多的安全与隐私泄露问题。在阐述移动云服务的基本概念、应用与安全问题的基础上,给出了其安全与隐私保护体系结构,主要围绕安全协议与认证、访问控制、完整性验证、移动可信计算和基于加密、匿名、混淆的隐私保护等关键技术,分析其研究现状,论述已有技术的优势和不足,并探讨了未来的研究方向。     

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。     

一种面向边缘计算的可扩展网络I/O框架

作为当前的研究热点,边缘计算可以在网络边缘部署计算、存储和网络资源,为用户提供云化服务,以满足新兴应用的处理需求。但是,当前网络I/O模型的扩展性问题使得运行在边缘节点上的边缘应用难以有效地利用丰富的边缘物理资源处理海量用户的网络请求。针对边缘计算场景下网络I/O模型的数据竞争和负载不均衡问题,提出了一种新的可扩展网络I/O模型。通过试验可以看出,利用该模型的边缘应用具有良好的性能和可扩展性。     

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.     

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。     

基于移动边缘计算的虚拟现实压缩与传输系统

针对基于云服务的虚拟现实对数据传输速率的高要求以及对传输时延的敏感性问题,提出并设计了基于移动边缘计算技术的云VR系统方案,主要包括基于视点的VR处理和混合数模传输优化。首先采用基于金字塔投影的用户视点感知动态推流方法实现基于视点的VR处理;然后引入混合数模技术对传输进行优化改进,并给出了启发式的资源分配优化算法;最后在长期演进(LTE)系统的基础上改造基站协议栈,融入移动边缘计算技术,实现了完整的移动边缘云VR系统。实验结果表明,与已有方案相比,所提方案能够实现更加稳健高效的传输,达到更好的VR用户体验。     

Dynamic game and reliable recommendation based transferring reputation mechanism for mobile cloud computing

The booming development of the mobile internet and cloud computing leads to the emerging of many mobile cloud platforms based services.However,since mobile users store lots of data and privacy information in the cloud when they are using the mobile cloud services,they are facing multiple increasingly serious security threats such as data leaks and privacy exposures.The data security and privacy protection was investigated in mobile cloud computing,aiming at the internal bad mouthing attacks and mobile attacks.A dynamic game and reliable recommendation based transferring reputation mechanism was proposed.First,a dynamic game based recommendation incentive mechanism was proposed.Secondly,a reliable recommendation reputation evaluation model was established based on the incentive mechanism.Last,a novel transferring reputation mechanism was proposed that combined the above mentioned incentive mechanism and reputation evaluation model.Simulation results demonstrate the proposed transferring reputation mechanism can defend against the internal bad mouthing attacks and mobile attacks effectively,enhance the credibility of mobile terminals and improve the data security and privacy protection of mobile cloud services.     

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.     

Pervasive data access in wireless and mobile computing environments

The rapid advance of wireless and portable computing technology has brought a lot of research interests and momentum to the area of mobile computing. One of the research focus is on pervasive data access. With wireless connections, users can access information at any place at any time. However, various constraints such as limited client capability, limited bandwidth, weak connectivity, and client mobility impose many challenging technical issues. In the past years, tremendous research efforts have been put forth to address the issues related to pervasive data access. A number of interesting research results were reported in the literature. This survey paper reviews important works in two important dimensions of pervasive data access: data broadcast and client caching. In addition, data access techniques aiming at various application requirements (such as time, location, semantics and reliability) are covered. Copyright © 2006 John Wiley & Sons, Ltd.     

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...     

区块链在轨道交通边缘计算网络中的应用

多接入边缘计算（multi-access edge computing,MEC）能为城市轨道交通中的计算密集型业务和时延敏感型业务提供高质量的服务能力,然而轨道交通边缘计算网络中的大量边缘设施暴露在开放式环境中,其隐私保护和传输安全面临着很大的挑战。区块链（blockchain）具有分布式账本、共识机制、智能合约、去中心化应用等功能特性,因此,区块链技术可以为分布式轨道交通边缘计算网络构建系统性的安全防护机制,从而保障网络安全和数据安全,实现高质量的城市轨道交通服务。首先,介绍了区块链的基本概念;其次,设计了轨道交通边缘计算网络架构,提出了融合区块链的轨道交通边缘计算网络安全防护机制和应用实例;最后,对该安全防护机制面临的问题和挑战进行了分析和展望。     

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.     

区块链网络安全保障:攻击与防御

随着区块链技术的迅猛发展,区块链系统的安全问题正逐渐暴露出来,给区块链生态系统带来巨大风险.通过回顾区块链安全方面的相关工作,对区块链潜在的安全问题进行了系统的研究.将区块链框架分为数据层、网络层、共识层和应用层4层,分析其中的安全漏洞及攻击原理,并讨论了增强区块链安全的防御方案.最后,在现有研究的基础上展望了区块链安...     

MobiSoC: a middleware for mobile social computing applications

Recently, we started to experience a shift from physical communities to virtual communities, which leads to missed social opportunities in our daily routine. For instance, we are not aware of neighbors with common interests or nearby events. Mobile social computing applications (MSCAs) promise to improve social connectivity in physical communities by leveraging information about people, social relationships, and places. This article presents MobiSoC, a middleware that enables MSCA development and provides a common platform for capturing, managing, and sharing the social state of physical communities. Additionally, it incorporates algorithms that discover previously unknown emergent geo-social patterns to augment this state. To demonstrate MobiSoC's feasibility, we implemented and tested on smart phones two MSCAs for location-based mobile social matching and place-based ad hoc social collaboration. Experimental results showed that MobiSoC can provide good response time for 1,000 users. We also demonstrated that an adaptive localization scheme and carefully chosen cryptographic methods can significantly reduce the resource consumption associated with the location engine and security on smart phones. A user study of the mobile social matching application proved that geo-social patterns can double the quality of social matches and that people are willing to share their location with MobiSoC in order to benefit from MSCAs.

A comparative study and workload distribution model for re‐encryption schemes in a mobile cloud computing environment

The researchers are using the various variations of re‐encryption schemes, which migrate the computational intensive re‐encryption jobs of mobile devices to the trusted entity/cloud. However, the messages are still encrypted and decrypted using the limited computational power of mobile devices. Our contribution in this paper is to propose a workload distribution model for re‐encryption schemes, which offloads the computational intensive operations, such as encryption and decryption on a trusted entity. Moreover, the proposed workload distribution model is compared with existing re‐encryption schemes of resource utilization on trusted entity and mobile device. The experimental results show substantial improvement in performance compared to the existing schemes.