关键词： Mobile edge computing   non-orthogonal multiple access   physical layer security   trajectory optimization   unmanned aerial vehicle  

摘要： Unmanned aerial vehicle (UAV) has been widely applied in internet-of-things (IoT) scenarios while the security for UAV communications remains a challenging problem due to the broadcast nature of the line-of-sight (LoS) wireless channels. This article investigates the security problems for dual UAV-assisted mobile edge computing (MEC) systems, where one UAV is invoked to help the ground terminal devices (TDs) to compute the offloaded tasks and the other one acts as a jammer to suppress the vicious eavesdroppers. In our framework, minimum secure computing capacity maximization problems are proposed for both the time division multiple access (TDMA) scheme and non-orthogonal multiple access (NOMA) scheme by jointly optimizing the communication resources, computation resources, and UAVs' trajectories. The formulated problems are non-trivial and challenging to be solved due to the highly coupled variables. To tackle these problems, we first transform them into more tractable ones then a block coordinate descent based algorithm and a penalized block coordinate descent based algorithm are proposed to solve the problems for TDMA and NOMA schemes, respectively. Finally, numerical results show that the security computing capacity performance of the systems is enhanced by the proposed algorithms as compared with the benchmarks. Meanwhile, the NOMA scheme is superior to the TDMA scheme for security improvement.

关键词： Task analysis   Servers   Optimization   Computational modeling   Resource management   Edge computing   Energy consumption   Mobile edge computing   computation offloading   multi-server   multi-task   deep reinforcement learning   deep deterministic policy gradient  

摘要： With the development of computer technology, computational-intensive and delay-sensitive applications are emerging endlessly, and they are limited by the computing power and battery life of Smart Mobile Devices (SMDs). Mobile edge computing (MEC) is a computation model with great potential to meet application requirements and alleviate burdens on SMDs through computation offloading. However, device mobility and server status variability in the multi-server and multi-task scenario bring challenges to the computation offloading. To cope with these challenges, we first propose a parallel task offloading model and a small area-based edge offloading scheme in MEC. Then, we formulate the optimization problem to minimize the completion time of all tasks, and transform the problem into a deep reinforcement learning-based offloading scheme by Markov decision approach. Furthermore, we present a deep deterministic policy gradient (DDPG) approach for obtaining the offloading strategy. Experimental results demonstrate that the DDPG- based offloading approach improves long-term performance by at least 19% in ultra-low latency, efficient usage of servers, and frequent mobility of SMDs over traditional strategies.

关键词： Internet of Things   cognitive radio   mobile edge computing   MEC services   spectrum management   energy consumption   mobility   analysis process   decision making  

摘要： The integration of cognitive radio (CR) in Internet of Things (IoT) is an effective step into the smart technology world. The capability of CR can effectively solve spectrum-related issues for IoT applications, but this association is still a big challenge that has led to a new research dimension of CR-based IoT. To this extend, in this paper the authors propose a novel distributed spectrum management approach based on mobile edge computing (MEC) technology in cooperative environment that enables CRIoT devices to share the unutilized spectrum efficiently. The simulation results show that the proposed solution achieves good performance in terms of spectrum access/sharing and maintains a balance energy consumption of CRIoT users within lower latency.

ISBN: (纸本)3030839435;9783030839437

关键词： 5G mobile communication   Device-to-device communication   Cellular networks   Network slicing   3G mobile communication   Business   Cloud computing   5G cellular networks   D2D communication   mobile edge computing   network slicing  

摘要： With the tremendous demand for connectivity anywhere and anytime, existing network architectures should be modified. To cope with the challenges that arise due to the increasing flood of devices/users and a diverse range of application requirements, new technologies and concepts must be integrated to enable their benefits. Service providers and business companies are looking for new areas of research to enhance overall system performance. This article gives a detailed survey about the recent 5G technologies, the solutions they provide, and the effect caused by their addition to current cellular networks. It is based on the three most important 5G concepts: Device to Device (D2D), Network Slicing (NS), and Mobile Edge Computing (MEC). This study proposes to design the future 5G networks by the integration of all three technologies. It is believed that spectrum efficiency, energy efficiency, and overall throughput will be greatly improved by using D2D. The system delay and computational load will be reduced as tasks will be handled by edge routers located at the base stations. Thus offloading the core network and the system capital expenses and operational expenses will be reduced significantly by slicing the network.

关键词： compute first networking   CFN   mobile edge computing   cloud computing   5G  

摘要： Edge computing has become an important innovative business model in the 5G era, especially its low latency feature, which is considered to be not available in traditional solutions. Therefore, edge computing can provide more service capabilities and has a wider range of application scenarios. However, the collaboration of computing power between edge computing and cloud computing in the central position has become a new technical problem. That is, it is necessary to achieve cloud-to-network collaboration, cloud-to-edge collaboration, or even edge-to-edge collaboration among edge computing, cloud computing, and network to achieve the optimisation of resource utilisation. On the basis of studying the compute distribution and scheduling requirements of edge computing, this paper introduces a compute network scheme based on the deep fusion of cloud, edge and network, called compute first networking (CFN). Firstly, we introduce the basic concept and related work of CFN. Then, we mainly discuss the framework and key technology of CFN. After that, we present some applications with respect to CFN. Finally, we discuss the challenges and opportunities in the area of compute first networking.

ISBN: (数字)9783030698935 ISBN: (纸本)9783030698928

关键词： Task analysis   Resource management   Mobile handsets   Edge computing   Servers   Approximation algorithms   Cloud computing   Mobile edge computing   computation offloading   resource allocation   energy efficient   polynomial time approximation scheme  

摘要： The edge cloud provides heterogeneous resources, such as cores, memory, and storage which are then allocated to mobile applications in mobile edge computing, which require multiple types of resources to execute. While current work on mobile edge computing focuses on tasks needing a single resource to run, we investigate the task allocation problem aiming to minimize total energy consumption while considering heterogeneous resource settings. Specifically, we consider the binary computation offloading mode, in which a task executes successfully as a whole. We formulate this as an integer programming problem, and transform that to the two subproblems of the resource allocation and the offloading decision. We propose a heuristic approach to solve the resource allocation subproblem and an approximation algorithm for the offloading decision subproblem. We show that our proposed approximation algorithm is a polynomial time approximation scheme, and hence it achieves a tradeoff between optimality loss and time complexity. Experimental results demonstrate that the performance of our proposed algorithm scales very well for multi-resource allocation in different conditions, and it achieves a good balance of performance and speed.

关键词： Streaming media   Quality of experience   Tutorials   Uplink   Resource management   Edge computing   Wireless networks   Wireless communications   5G networks   Internet of Things   mobile edge computing   edge analytics   video analytics   caching   task offloading   video streaming   quality of experience  

摘要： Future wireless networks will provide high-bandwidth, low-latency, and ultra-reliable Internet connectivity to meet the requirements of different applications, ranging from virtual reality to the Internet of Things. To this aim, edge caching, computing, and communication (edge-C3) have emerged to bring network resources (i.e., bandwidth, storage, and computing) closer to end users. Edge-C3 improves the network resource utilization as well as the quality of experience (QoE) of end users. Recently, several video-oriented mobile applications (e.g., live content sharing, gaming, and augmented reality) have leveraged edge-C3 in diverse scenarios involving video streaming in both the downlink and the uplink. Hence, a large number of recent works have studied the implications of video analysis and streaming through edge-C3. This article presents an in-depth survey on video edge-C3 challenges and state-of-the-art solutions in next-generation wireless and mobile networks. Specifically, it includes: a tutorial on video streaming in mobile networks (e.g., video encoding and adaptive bit-rate streaming);an overview of mobile network architectures, enabling technologies, and applications for video edge-C3;video edge computing and analytics in uplink scenarios (e.g., architectures, analytics, and applications);and video edge caching, computing and communication methods in downlink scenarios (e.g., collaborative, popularity-based, and context-aware). A new taxonomy for video edge-C3 is proposed and the major contributions of recent studies are first highlighted and then systematically compared. Finally, several open problems and key challenges for future research are outlined.

关键词： Mobile edge computing  

摘要： In this paper, we study an RF energy harvesting mobile edge computing network based on a SIMO/MISO system and NOMA schemes over Nakagami-m fading. Specifically, a multi-antenna user harvests RF energy from a power station by using a selection combining/maximal ratio combining scheme and offload its tasks to two MEC servers through downlink NOMA by employing transmit antenna selection/maximal ratio transmission scheme. Accordingly, we investigate the performance of six schemes, namely SC-TAS1, SC-TAS1, MRC-TAS1, MRC-TAS2, SC-MRT, and MRC-MRT, for this considered system. To evaluate the performance, exact closed- form expressions of successful computation probability are derived. We further propose the optimal algorithm to find the best parameter sets to achieve the best performance. Moreover, the impacts of the network parameters on the system performance for these schemes are investigated. Finally, the simulation results are also provided to verify the accuracy of our analysis. Copyright © 2021 Dac-Binh Ha et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited. doi:10.4108/eai.28-4-2021.169425