关键词： Mobile edge computing  

摘要： From 2015 to the end of 2016, the Internet set off a wave of payment for knowledge. Pay-for-knowledge platforms such as Fenda, iGet, and Qianliao went online quickly, and platforms such as Himalaya FM, Zhihu, and Qingting FM gathered to launch paid columns..e number of users increased rapidly, and payment for knowledge was considered to have reached the trend of development. This article aims to study the introduction of edge computing in the mobile information system into the existence and inevitability of the knowledge payment platform;analyze the advantages, dilemmas, and optimization paths of the knowledge payment platform;and try to provide a theoretical reference for promoting its development. This article puts forward an explanation of the related content of mobile edge computing and RFID technology overview, using comparative experiment and behavior analysis methods. The experimental results show that there are 98 people under the age of 18 in the questionnaire, accounting for 19.1% of the total, 201 people aged 18-29, accounting for 39.1% of the total, 142 people aged 30-39, accounting for 27.6% of the total, and 73 people over 40 years old, accounting for 14.2% of the total number. It can be seen from the data that the sample age is mainly concentrated in the 18-29 years old, followed by the 30-39 years old;the sample age is biased towards young people. It has well completed the continuous use behavior analysis of the knowledge payment platform based on edge computing under the mobile information system.

关键词： Mobile edge computing  

摘要： Over the last few years, the DRL paradigm has been widely adopted for 5G and beyond network optimization because of its extreme adaptability to many different scenarios. However, collecting and processing learning data entail a significant cost in terms of communication and computational resources, which is often disregarded in the networking literature. In this work, we analyze the cost of learning in a resource-constrained system, defining an optimization problem in which training a DRL agent makes it possible to improve the resource allocation strategy but also reduces the number of available resources. Our simulation results show that the cost of learning can be critical when evaluating DRL schemes on the network edge and that assuming a cost-free learning model can lead to significantly overestimating performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： In the current era of the Internet of Things (IoT), various devices can provide more services by connecting to the Internet. However, the explosive growth of connected devices will cause the cloud core overload and significant network delays. To overcome these problems, the Mobile Edge Computing (MEC) network is proposed to provide most of the computing and storage near the radio access network to reduce the traffic of the core cloud network and provide lower latency for the terminal. Mobile edge computing can work with third parties to develop multiple services, such as mobile big data analysis and context-aware services. However, when there is a large amount of popular data accessed in a short period, the system must generate many replicas, which will not only reduce access efficiency but also cause additional traffic overhead. To improve the above problems, an Adaptive Replica Configuration Mechanism (ARCM) is proposed in this paper to predict the popularity of the file and make a replica to the low-blocking node. This method spreads the subsequent access workload by copying the popular file in advance to improve the overall performance of the system. © 2021, CC BY.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) provides user equipment (UE) with computing capability through wireless networks to improve the quality of experience (QoE). The scenario with multiple base stations and multiple mobile users is modeled and analyzed. The optimization strategy of task offloading with wireless and computing resource management (TOWCRM) in mobile edge computing is considered. A resource allocation algorithm based on an improved graph coloring method is used to allocate wireless resource blocks (RBs). The optimal solution of computing resource is obtained by using KKT conditions. To improve the system utility, a semi-distributed TOWCRM strategy is proposed to obtain the task offloading decision. Theoretical simulations under different system parameters are executed, and the proposed semi-distributed TOWCRM strategy can be completed with finite iterations. Simulation results have verified the effectiveness of the proposed algorithm.

关键词： Mobile edge computing  

摘要： Federated learning (FL) enables devices in mobile edge computing (MEC) to collaboratively train a shared model without revealing the local data. Gradient compression could be applied to FL to alleviate the communication overheads but the existing schemes still face challenges. To deploy green MEC, we propose FedGreen, which enhances the original FL with fine-grained gradient compression to control the total energy consumption of the devices. Specifically, we introduce the relevant operations including device-side gradient reduction and server-side element-wise aggregation to facilitate the gradient compression in FL. According to a public dataset, we evaluate the contributions of the compressed local gradients with respect to different compression ratios. Furthermore, we investigate a learning accuracy-energy efficiency tradeoff problem and the optimal compression ratio and computing frequency are derived for each device. Experimental results show that given the 80% test accuracy requirement, compared with the baseline schemes, FedGreen reduces at least 32% of the total energy consumption of the devices. Copyright © 2021, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： By offloading computing tasks to the edge of mobile network, mobile edge computing(MEC) solves the shortcomings of mobile devices in computing capacity and storage capacity. In order to reduce the computing delay of mobile terminal devices and the degree of workload balancing in MEC servers, a computing offloading solution based on information entropy immune algorithm is proposed. Firstly, the system model, delay model, load model and objective optimization model are constructed. Then, a task offloading scheme based on immune optimization is proposed .Finally, this scheme is compared with the Benchmark scheme and the offloading scheme in the literature. Simulation experiments show that the proposed scheme can effectively reduce the average offloading delay, and can effectively balance the load between MEC servers. At the same time, compared with the schemes in the literature, the performance of the scheme is improved to a certain extent. © 2021, CC BY.

关键词： Mobile edge computing   Multicast   Stochastic optimization   Resource allocation  

摘要： Mobile edge computing (MEC) is rising as a key technology for computation-intensive and delay-sensitive applications. Many works have focused on MEC, but most of them only consider unicast scenarios, and ignore multicast issues. The reason is that MEC aiming at personalized computing of users conflicts with multicast which demands the same data stream. This makes MEC and multicast seem inconsistent. However, in fact, there will be lots of services, whose computation process is different but the result may be same (e.g. media push), which can greatly benefit from multicast over MEC and make the combination of multicast and MEC meaningful. In this paper, we first consider challenges and propose multicast-aware resource allocation for MEC, which jointly optimizes computing and caching in multicast scenarios. We formulate the problem by considering user request, network communication, service caching and service computing. But this model is knotty because it is an optimization problem with mixed discrete and continuous variables. Besides its optimization objective is the average value over a long time. Considering the complexity, we first transform the problem into an online optimization, which jointly minimizes the average time delay and energy consumption, by stochastic optimization. Then we separate the discrete variables and continuous variable, and decompose the problem into two subproblems. By solving subproblems, an efficient online algorithm called MA-ECC is proposed. Finally, we compare it with other three baseline methods, and result shows that MA-ECC can effectively reduce service latency while still keeping energy consumption low.

关键词： UAV   mobile edge computing   differential evolution algorithm   K-Means   edge data collection  

摘要： In the IoT(Internet of Things)system,the introduction of UAV(Unmanned Aerial Vehicle)as a new data collection platform can solve the problem that IoT devices are unable to transmit data over long distances due to the limitation of their battery ***,the unreasonable distribution of UAVs will still lead to the problem of the high total energy consumption of the *** this work,to deal with the problem,a deployment model of a mobile edge computing(MEC)system based on multi-UAV is *** goal of the model is to minimize the energy consumption of the system in the process of data transmission by optimizing the deployment of *** DEVIPSK(differential evolution algorithm with variable population size based on a mutation strategy pool initialized by K-Means)is proposed to solve the *** DEVIPSK,the population is initialized by K-Means to obtain better initial positions of ***,considering the limitation of the fixed mutation strategy in the traditional evolutionary algorithm,a mutation strategy pool is used to update the positions of *** experimental results show the superiority of the DEVIPSK and provide guidance for the deployment of UAVs in the field of edge data collection in the IoT system.

关键词： fog computing   edge computing   mobile edge computing   resource allocation,offloading   software defined networks   smart city   smart grid  

摘要： Smart cities improve the quality of life of people by utilizing the benefits of Information and communication technology(ICT)and the Internet of things(IoT).The applications of the smart city often rely on the cloud for *** doubt cloud provides an ample amount of resources as a service but still it has limitations in terms of unreliable latency,mobility,and location awareness due to their multi-hop distance from the IoT *** computing avoids these limitations by providing services nearer to the *** this work we investigate the already proposed IoT-Fog based application specific smart city architectures and review them based on scalability,heterogeneity,mobility,energy conservation,latency,and ***,we discuss the applications and highlight the challenges that fog computing *** also present a case study of a smart city scenario with multiple applications of IoT.

关键词： Mobile Edge Computing   Vehicular cloud   Vehicular micro cloud   Data management  

摘要： Edge computing is becoming a major building block of next generation 5G/6G networks. However, infrastructure might not always be available because of slow deployment. At the same time, vehicular networks are becoming a reality now and cars are being equipped with a variety of short-range communication devices. The idea of vehicular micro clouds is to turn cars into (virtual) edge computing infrastructure. One of the challenging questions in this domain is to maintain data within and among such micro clouds. In this paper, we focus on this task and present a novel solution for such data exchange between vehicular micro clouds. For efficient operation, the dwell times of cars in such a micro cloud need to be known or accurately predicted. In an extensive study based on trace data, we investigate the distribution of dwell times of cars at intersections. We make use of this distribution as an input for designing an improved data exchange algorithm. As not all intersections are the same, adding additional variance further benefits the solution. We evaluated our algorithm in different vehicular densities, and we observed that we could maintain data 22-208% longer within the micro clouds using our new algorithm. Overall, our results show that our algorithm clearly outperforms previous solutions.