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： 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 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： 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： 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： Mobile Edge Computing   Multimedia   Internet of Things   Survey  

摘要： Recently, to provide a low-latency mobile computing platform, Mobile Edge Computing (MEC) is proposed. In this paper, we first summarized the feature capabilities of MEC, such as content distribution and caching, computational offloading and multimedia Internet of Things (IoT). Then, to understand recent research efforts of multimedia MEC, we briefly highlight the research efforts in terms of above three capabilities: how to achieve edge caching in video distribution, how to schedule computational offloading to the cloud and how the communication quality degradation affects to the user experience of multimedia IoT. Finally, we addressed the emerging research issues of multimedia MEC to improve reliability and robustness of multimedia MEC.

关键词： Blockchain   mobile edge computing   therapy   IoT  

摘要： Mobile edge computing (MEC) is being introduced and leveraged in many domains, but few studies have addressed MEC for secure in-home therapy management. To this end, this paper presents an in-home therapy management framework, which leverages the IoT nodes and the blockchain-based decentralized MEC paradigm to support low-latency, secure, anonymous, and always-available spatiotemporal multimedia therapeutic data communication within an on-demand data-sharing scenario. To the best of our knowledge, this non-invasive, MEC-based IoT therapy platform is first done by our group. This platform can provide a full-body joint range of motion data for physically challenged individuals in a decentralized manner. With MEC, the framework can provide therapy diagnostic and analytical data on demand to a large portion of humanity who are either born with disabilities or became disabled due to accidents, war-time injuries, or old age. For security, the framework uses blockchain-Tor-based distributed transactions to preserve the therapeutic data privacy, ownership, generation, storage, and sharing. Our initial test results from a complete implementation of the framework show that it can support a sufficiently large number of users without considerable increase in mean processing time.

关键词： Internet of Thing   low power wide area network   mobile edge computing   remote software update   trusted connection  

摘要： The Internet of Things (IoT) leads to intelligent services by collecting information from tiny sensor devices. In recent years, storage-less sensing devices have been used to implement IoT services. They depend on delivered software from a network server to operate service functions, and IoT services are based on collected user information. Therefore, it is important to maintain trusted connections during software delivery or data transmission. If a network connection is untrustworthy, stable data transmission cannot be achieved. Untrustworthy data connections cause many problems in IoT services. Therefore, this paper proposes a software update method in trusted connection of IoT networking. The proposed method employs a low-power wide area network (LPWAN) as a long-range IoT networking technology and uses a mobile edge cloud to improve computing efficiency in an access network that consists of IoT devices with insufficient resources. In the proposed method, the mobile edge cloud is integrated into a gateway and processes sensing data and remote software updates of LPWAN. IoT devices can receive software functions from the mobile edge cloud. The proposed method analyzes statistical information about connections in an access network and determines the LPWAN trusted connections. Then, software updates can be performed over the trusted connection. Using trusted connections leads to an increased packet delivery rate and reduced transmission energy consumption. The proposed method is compared with currently available systems through computer simulation and the proposed method's efficiency is validated.

关键词： 5G   ultra-reliable low latency communications   mobile edge computing   computation offloading  

摘要： The ultra-reliable low latency communications (uRLLC) in the fifth generation mobile communication system aims to support diverse emerging applications with strict requirements of latency and reliability. Mobile edge computing (MEC) is considered as a promising solution to reduce the latency of computation-intensive tasks leveraging powerful computing units at short distance. The state-of-art work on task offloading to MEC mainly focuses on the tradeoff between latency and energy consumption, rather than reliability. In this paper, the tradeoff between the latency and reliability in task offloading to MEC is studied. A framework is provided, where user equipment partitions a task into sub-tasks and offloads them to multiple nearby edge nodes (ENs) in sequence. In this framework, we formulate an optimization problem to jointly minimize the latency and offloading failure probability. Since the formulated problem is nonconvex, we design three algorithms based on heuristic search, reformulation linearization technique and semi-definite relaxation, respectively, and solve the problem through optimizing EN candidates selection, offloading ordering and task allocation. Compared with the previous work, the numerical simulation results show that the proposed algorithms strike a good balance between the latency and reliability in uRLLC. Among them, the Heuristic Algorithm achieves the best performance in terms of the latency and reliability with the minimal complexity.