关键词： Mobile Edge Computing   Software Defined Network   Network Function Virtualization   Massive IoT   Synchronization  

摘要： This paper presents a novel and efficient data transmission scheme based on mobile edge computing for the massive IoT environments which should support various type of services and devices. Based on an accurate and precise synchronization process, it maximizes data transmission throughput, and consistently maintains a flow's latency. To this end, the proposed efficient software defined data transmission scheme (ESD-DTS) configures and utilizes synchronization zones in accordance with the 4 usage cases, which are end node-to-end node (EN-EN), end node-to-cloud network (EN-CN), end node-to-Internet node (EN-IN), and edge node-to-core node (EdN-CN);and it transmit the data by the required service attributes, which are divided into 3 groups (low-end group, medium-end group, and high-end group). In addition, the ESD-DTS provides a specific data transmission method, which is operated by a buffer threshold value, for the low-end group, and it effectively accommodates massive IT devices. By doing this, the proposed scheme not only supports a high, medium, and low quality of service, but also is complied with various 5G usage scenarios. The essential difference between the previous and the proposed scheme is that the existing schemes are used to handle each packet only to provide high quality and bandwidth, whereas the proposed scheme introduces synchronization zones for various type of services to manage the efficiency of each service flow. Performance evaluations show that the proposed scheme outperforms the previous schemes in terms of throughput, control message overhead, and latency. Therefore, the proposed ESD-DTS is very suitable for upcoming 5G networks in a variety of massive IoT environments with supporting mobile edge computing (MEC).

关键词： Mobile edge computing  

摘要： In this paper, we consider a wireless powered mobile edge computing (MEC) network, where the distributed energy-harvesting wireless devices (WDs) are powered by means of radio frequency (RF) wireless power transfer (WPT). In particular, the WDs follow a binary computation offloading policy, i.e., data set of a computing task has to be executed as a whole either locally or remotely at the MEC server via task offloading. We are interested in maximizing the (weighted) sum computation rate of all the WDs in the network by jointly optimizing the individual computing mode selection (i.e., local computing or offloading) and the system transmission time allocation (on WPT and task offloading). The major difficulty lies in the combinatorial nature of multi-user computing mode selection and its strong coupling with transmission time allocation. To tackle this problem, we propose a joint optimization method based on the ADMM (alternating direction method of multipliers) decomposition technique. Simulation results show that the proposed method can efficiently achieve near-optimal performance under various network setups, and significantly outperform the other representative benchmark methods considered. Besides, using both theoretical analysis and numerical study, we show that the proposed method enjoys low computational complexity against the increase of networks size. Copyright © 2018, The Authors. All rights reserved.

关键词： Autonomous vehicles   digital maps   connected car   mobile edge computing   SLAM  

摘要： One major key to autonomous driving is reliable knowledge about the vehicle's surroundings. In complex situations like urban intersections, the vehicle's on-board sensors are often unable to detect and classify all features of the environment. Therefore, high-precision digital maps are widely used to provide the vehicle with additional information. In this article, we introduce a system which makes use of a mobile edge computing architecture (MEC) for computing digital maps on infrastructure-based, distributed computers. In cooperation with the mobile network operator Vodafone an LTE test field is implemented at the Aldenhoven Testing Center (ATC). The proving ground thus combines an urban crossing with the MEC capabilities of the LTE test field so that the developed methods can be tested in a realistic scenario. In the near future the LTE test field will be equipped with the new 5G mobile standard allowing for fast and reliable exchange of map and sensor data between vehicles and infrastructure.

关键词： Mobile cloud computing   Mobile edge computing   Context adaptation   Context awareness  

摘要： Markets for mobile applications offer myriads of apps ranging from simple to quite demanding ones. The latter are on the rise since every new generation of smartphones is equipped with more resources (CPU, memory, bandwidth, energy) to even allow re-source-demanding services like speech- or face recognition to be executed locally on a device. But compared to their stationary counterparts, mobile devices remain comparatively limited in terms of resources. Because of this, current approaches aim at extending mobile device capabilities with computation and storage resources offered by cloud services or other nearby devices. This paradigm, known as mobile cloud computing (MCC), is challenged by the dynamically changing context of mobile devices, which developers are required to take into account to decide, e.g., which application parts are when to offload. To rise to such and similar challenges we introduce the concept of Generic Context Adaptation (GCA), a data mining process that facilitates the adaptation of (mobile) applications to their current and future context. Moreover, we evaluate our approach with real usage data provided by the Nokia Mobile Data Challenge (MDC) as well as with CloudAware, a context-adaptive mobile middleware for MCC that supports automated and context-aware self-adaptation techniques.

关键词： Mobile edge computing  

摘要： Mobile Edge Computing (MEC) pushes computing functionalities away from the centralized cloud to the network edge, thereby meeting the latency requirements of many emerging mobile applications and saving backhaul network bandwidth. Although many existing works have studied computation offloading policies, service caching is an equally, if not more important, design topic of MEC, yet receives much less attention. Service caching refers to caching application services and their related databases/libraries in the edge server (e.g. MEC-enabled BS), thereby enabling corresponding computation tasks to be executed. Because only a small number of application services can be cached in resource-limited edge server at the same time, which services to cache has to be judiciously decided to maximize the edge computing performance. In this paper, we investigate the extremely compelling but much less studied problem of dynamic service caching in MEC-enabled dense cellular networks. We propose an efficient online algorithm, called OREO, which jointly optimizes dynamic service caching and task offloading to address a number of key challenges in MEC systems, including service heterogeneity, unknown system dynamics, spatial demand coupling and decentralized coordination. Our algorithm is developed based on Lyapunov optimization and Gibbs sampling, works online without requiring future information, and achieves provable close-to-optimal performance. Simulation results show that our algorithm can effectively reduce computation latency for end users while keeping energy consumption low. Copyright © 2018, The Authors. All rights reserved.

关键词： Security   Privacy   Cloud computing   Fog computing   Mobile edge computing   Mobile cloud computing  

摘要： For various reasons, the cloud computing paradigm is unable to meet certain requirements (e.g. low latency and jitter, context awareness, mobility support) that are crucial for several applications (e.g. vehicular networks, augmented reality). To fulfill these requirements, various paradigms, such as fog computing, mobile edge computing, and mobile cloud computing, have emerged in recent years. While these edge paradigms share several features, most of the existing research is compartmentalized;no synergies have been explored. This is especially true in the field of security, where most analyses focus only on one edge paradigm, while ignoring the others. The main goal of this study is to holistically analyze the security threats, challenges, and mechanisms inherent in all edge paradigms, while highlighting potential synergies and venues of collaboration. In our results, we will show that all edge paradigms should consider the advances in other paradigms. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Information-centric networking   mobile edge computing   video multicast   wireless network virtualization  

摘要： To support the massive content delivery, a novel virtualized heterogeneous network framework is proposed to enable content transcoding, caching, and multicast. In addition, a virtual multi-resources allocation problem is formulated to jointly optimize the utilities of computing, caching, and communication for the proposed framework. Simulations are conducted to show the effectiveness of the proposed scheme.

关键词： Mobile edge computing  

摘要： In the evolution toward fifth-generation networks, Mobile Edge Computing (MEC) is an emerging paradigm, conceived to meet the ever-increasing computational demands of mobile applications. Within the access range of mobile devices, the MEC technique promises the enablement of efficient Mobile Cloud Computing services. In an MEC system, virtual machine (VM) migration is a key issue;VM migration is the process of moving a VM from an edge node to another edge node. To improve service quality and system performance, the VM migration method has a dual focus on the MEC system's computation and communication resources. In this study, we formulate the VM migration problem as a one-on-one contract game model and develop a learning-based price control mechanism to effectively handle the MEC's resource. By using the game methodology and learning process, our approach is able to capture the dynamics of MEC systems, and it interacts continually with an unknown system environment. Finally, extensive simulation results are provided to demonstrate the capability of the proposed approach in achieving, with respect to existing MEC schemes, both higher resource utilization and system throughput, as well as reduced service drop ratio and reduced service delay.

关键词： Mobile edge computing   resource allocation   fairness   system throughput maximization   minimum rate requirement  

摘要： Communication resource allocation is important for improving the performance of users in mobile edge computing (MEC) scenarios. In existing studies, the users in the MEC system typically suffer from unfair resource allocation, which results in the inefficient resource utilization and degraded user performance. To address this challenge, in this paper we propose a fair resource allocation approach to maximize the overall network throughput, under the constraint of each mobile user's minimum transmission rate. We formulate the problem as a fair Nash bargaining resource allocation game, and the existence and uniqueness of the solution to this game model are analyzed. By adopting the time-sharing variable, we obtain the near optimal bargaining resource allocation strategy for the mixed integer nonlinear programming optimization. The user's priority is further considered in the iterative implementation of the proposed algorithm by considering the time delay constraint of users. Simulation results show that the proposed scheme outperforms existing methods in terms of resource allocation fairness and overall system throughput.

关键词： Caching   computation offloading   mobile edge computing   energy efficient  

摘要： While augment reality applications are becoming popular, more and more data-hungry and computation-intensive tasks are delay-sensitive. Mobile edge computing is expected to an effective solution to meet the low latency demand. In contrast to previous work on mobile edge computing, which mainly focus on computation offloading, this paper introduces a new concept of task caching. Task caching refers to the caching of completed task application and their related data in edge cloud. Then, we investigate the problem of joint optimization of task caching and offloading on edge cloud with the computing and storage resource constraint. We formulate this problem as mixed integer programming which is hard to solve. To solve the problem, we propose efficient algorithm, called task caching and offloading (TCO), based on alternating iterative algorithm. Finally, the simulation experimental results show that our proposed TCO algorithm outperforms others in terms of less energy cost.