关键词： Fiber wireless network   mobile edge computing   collaborative computation offloading   scaling computing capability   transmit power allocation   residual battery rate  

摘要： With the emergence of smart mobile devices (SMDs) and mobile applications, the cloud-mobile edge computing (MEC) collaborative computation offloading (CMCCO) scheme, i.e., offloading the computation-intensive task from the local SMD to either the MEC server, or the remote mobile cloud computing server (MCC), is widely identified as a promising candidate under the conflict between SMDs' limited computing ability and computing-intensive application requiring higher energy consumption. Meanwhile, the existing CMCCO scenario over integrated cloud-MEC Fiber Wireless broadband access networks (CM-FiWi) architecture, by generally fixing computing ability and transmitting power, still achieves higher computation offloading overhead in terms of task's aggregate response time and SMD's energy consumption. In light of this, the energy-aware collaborative computation offloading (EA-CCO) paradigm with very diverse types of computation tasks over CM-FiWi broadband access network is provided in this paper. An iterative searching algorithm for collaborative computation offloading scheme (ISA-CCO) is proposed as a solution to obtain minimized task offloading overhead, which jointly takes scaling computing ability, variable transmit power, and residual battery rate into considerations. Extensive numerical results demonstrate that the proposed solution outperforms the traditional paradigms, e.g., optimal enumeration collaborative computation offloading scheme (OECCO), approximation collaborative computation offloading algorithm (ACCO), and game theoretic collaborative computation offloading scheme (GT-CCO). More specially, the proposed ISA-CCO scheme obviously achieves lower overall task offloading overhead than those fixed transmit power and computational frequency scaling.

关键词： Mobile edge computing  

摘要： Exploiting the idle computation resources of mobile devices in mobile edge computing (MEC) system can achieve both channel diversity and computing diversity as mobile devices can offload their computation tasks to nearby mobile devices in addition to MEC server embedded access point (AP). In this paper, we propose a non-orthogonal multiple-access (NOMA)-A ided cooperative computing scheme in a basic three-node MEC system consisting of a user, a helper, and an AP. In particular, we assume that the user can simultaneously offload data to the helper and the AP using NOMA, while the helper can locally compute data and offload data to the AP at the same time. We study two optimization problems, energy consumption minimization and offloading data maximization, by joint communication and computation resource allocation of the user and helper. We find the optimal solutions for the two non-convex problems by some proper mathematical methods. Simulation results are presented to demonstrate the effectiveness of the proposed schemes. Some useful insights are provided for practical designs. Copyright © 2019, The Authors. All rights reserved.

关键词： latency-sensitive applications   Lyapunov optimization   mobile edge computing   service deployment   task scheduling  

摘要： Currently, Mobile edge computing (MEC) is facing a great challenge that is how to make full use of edge resources to provide a seamless support for compute-intensive latency-sensitive applications. Prior studies often make a simple assumption that tasks can be executed upon every edge server, but the assumption does not hold in practical scenarios. Because a specific application task often corresponds to a certain service that provides the corresponding running environment, whereas an edge server only has limited resources and cannot offer too many services. How to decide service deployment of so many types of services among multiple edge servers is also a big challenge. To address the challenge, we study dynamic service deployment for latency-sensitive applications. We first model the long-term budget-constrained latency minimization problem as a multi-slot latency minimization problem based on the Lyapunov framework. By doing this, the hardness of a problem is significantly reduced, since we never require future information to solve the long-term optimization. Furthermore, we extend our study by joining the task scheduling optimization, where every edge server is fully utilized in an even more efficient collaborative manner. Our extensive experiments show that the proposed algorithms can bring short latency with low cost.

关键词： Mobile edge computing  

摘要： We investigate the problem of computation offloading in a mobile edge computing architecture, where multiple energy-constrained users compete to offload their computational tasks to multiple servers through a shared wireless medium. We propose a multi-agent deep reinforcement learning algorithm, where each server is equipped with an agent, observing the status of its associated users and selecting the best user for offloading at each step. We consider computation time (i.e., task completion time) and system lifetime as two key performance indicators, and we numerically demonstrate that our approach outperforms baseline algorithms in terms of the trade-off between computation time and system lifetime. Copyright © 2019, The Authors. All rights reserved.

关键词： Mobile edge computing   Edge content delivery   Edge content update   Edge server   Cache pool   Content server  

摘要： Content delivery network (CDN) has gained increasing popularity in recent years for facilitating content delivery. Most existing CDN-based works upload the content generated by mobile users to the cloud data center firstly. Then, the cloud data center delivers the content to the proxy server. Finally, the mobile users request the required content from the proxy server. However, uploading all the collected content to the cloud data center increases the pressure on the core network. In addition, it also wastes a lot of bandwidth resources because most of the content does not have to be uploaded. To make up for the shortcomings of existing CDN-based works, this article proposes an edge content delivery and update (ECDU) framework based on mobile edge computing architecture. In the ECDU framework, we deploy a number of content servers to store raw content collected from mobile users, and cache pools to store content that frequently requested at the edge of the network. Thus, it is not necessary to upload all content collected by mobile users to the cloud data center, thereby alleviating the pressure of the core network. Based on content popularity and cache pool ranking, we also propose edge content delivery (ECD) and edge content update (ECU) schemes. The ECD scheme is to deliver content from cloud data center to cache pool, and the ECU scheme is to mitigate the content to appropriate cache pools in terms of its request frequency and cache pool ranking. Finally, a representative case study is provided and several open research issues are discussed.

关键词： Mobile edge computing  

摘要： —To fulfill the requirements of various emerging applications, the future sixth generation (6G) mobile network is expected to be an innately intelligent, highly dynamic, ultradense heterogeneous network that interconnects all things with extremely low-latency and high speed data transmission. It is believed that artificial intelligence (AI) will be the most innovative technique that can achieve intelligent automated network operations, management and maintenance in future complex 6G networks. Driven by AI techniques, device-to-device (D2D) communication will be one of the pieces of the 6G jigsaw puzzle. To construct an efficient implementation of intelligent D2D in future 6G, we outline a number of potential D2D solutions associating with 6G in terms of mobile edge computing, network slicing, and Non-orthogonal multiple access (NOMA) cognitive Networking.I. INTRODUCTION Copyright © 2019, The Authors. All rights reserved.

关键词： Mobile edge computing   Wireless energy transfer   NOMA   IoT  

摘要： The mobile edge computing (MEC) is combined with wireless energy transfer (WET) to enhance the performance of wireless devices in Internet of Things (IoT) systems, which is called wireless powered mobile edge computing (WP-MEC). The sustainable energy supply and adequate computing capabilities are provided for wireless devices in WP-MEC. In this paper, we propose an online energy consumption minimization (OECM) algorithm for multiple IoT devices with latency constraints in WP-MEC environment. The nonorthogonal multiple access (NOMA) technique is used to address the preamble collision where two or more devices may select the same preamble. Based on Lyapunov optimization framework, optimal results of the time allocation for energy transfer and data transmission, the proportions of offloading tasks, data transmit powers and CPU-cycle frequencies are achieved in each time block by an iterative algorithm which leverages a convex approximation approach. The theoretical performance analysis shows that there is an upper bound of the system energy consumption. Simulation results verify that our proposed OECM algorithm outperforms the scheme without wireless energy transfer and other benchmark algorithms. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Cyber security   mobile edge computing   online learning   multi-armed bandit   jamming  

摘要： To accommodate heterogeneous tasks for the Internet of Things (IoT), the emerging mobile edge paradigm extends computing services from the cloud to the edge, but at the same time exposes new challenges on security. In this context, the present paper deals with online security-aware edge computing under jamming attacks. Leveraging online learning tools, novel approaches are developed to cope with adversarial worst-case attacks, and stochastic attacks with random attack strategies. Rather than relying on extra bandwidth and power resources to evade jamming attacks, the resultant algorithms select the most reliable server to offload computing tasks with minimal security concerns. It is analytically established that without any prior information on future jamming and server security risks over a time horizon T, the proposed schemes can achieve O(root T) regret. Information sharing among devices can accelerate the security-aware computing tasks, quantified by what is termed "value of cooperation." Effectiveness of the proposed schemes is tested on synthetic and real datasets.

关键词： 5G   Cooperative offloading   FiWi enhanced HetNets   Mobile edge computing  

摘要： Novel 5G applications will require the processing of large amounts of computation-intensive, delay-sensitive tasks. Mobile devices, however, are subject to limited hardware resources (e.g., battery life, CPU, and storage) and therefore may fall short of satisfying the stringent latency requirements of complex tasks. To fill the gap between the increasing demands of computation-intensive, delay-sensitive tasks driven by emerging 5G applications and the availability of limited resources on smart mobile devices, we introduce our proposed architecture of multi-access computing (MEC) enabled Fiber-Wireless (FiWi) LTE enhanced HetNets, enabling the coexistence and cooperation between conventional (remote) cloud and MEC servers. After presenting our analytical framework to evaluate the energy-delay performance, we propose our distributed cooperative offloading strategy with the objective of minimizing the average response time for mobile users.

关键词： Cloud computing   Mobile edge computing   Sensor-clouds   WSNs  

摘要： Sensor-clouds are a combination of wireless sensor networks (WSNs) and cloud computing. The emergence of sensor-clouds has greatly enhanced the computing power and storage capacity of traditional WSNs via exploiting the advantages of cloud computing in resource utilization. However, there are still many problems to be solved in sensor-clouds, such as the limitations of WSNs in terms of communication and energy, the high latency, and the security and privacy issues due to applying a cloud platform as the data processing and control center. In recent years, mobile edge computing has received increasing attention from industry and academia. The core of mobile edge computing is to migrate some or all of the computing tasks of the original cloud computing center to the vicinity of the data source, which gives mobile edge computing great potential in solving the shortcomings of sensor-clouds. In this paper, the latest research status of sensor-clouds is briefly analyzed and the characteristics of the existing sensor-clouds are summarized. After that we discuss the issues of sensor-clouds and propose some applications, especially a trust evaluation mechanism and trustworthy data collection which use mobile edge computing to solve the problems in sensor-clouds. Finally, we discuss research challenges and future research directions in leveraging mobile edge computing for sensor-clouds. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.