scholarly journals Evaluation and Analysis of Traditional Physical Training by Using Mobile Edge Computing and Software-Defined Networking

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wenwen Pan ◽  
Jianzhi Wang ◽  
Jingsheng Ji
Keyword(s):  
Physical Training ◽  
Rapid Development ◽  
Software Defined Networking ◽  
The Body ◽  
Edge Computing ◽  
Training Data ◽  
Mobile Edge Computing ◽  
Centralized Control ◽  
Traffic Scheduling ◽  
Computing Technique

The body health plays an important metric in people’s everyday life, and it directly determines whether people have the ability to preferably contribute to the society. In fact, the physical training is a universal sport to enhance the body health. Therefore, the evaluation and analysis of physical training become particularly significant. With the rapid development and emerging of new techniques and networking paradigms, the traditional offline physical training evaluation and analysis cannot be performed well. Instead, this paper uses Mobile Edge Computing (MEC) and Software-Defined Networking (SDN) to implement the evaluation and analysis of physical training, shortened for MSPT, where MEC is the new computing technique and SDN is the new networking paradigm. The proposed MSPT includes two parts. At first, the physical training data from different mobile devices are migrated into the edge server for computing according to the current condition, in which the game theory is used to complete the task scheduling. Then, SDN is responsible for the global scheduling in the centralized control manner, in which the multigranularity scheduling strategy is used to handle the traffic between the SDN controller and edge computing server. The experiments are driven by OMNet, including three aspects of evaluation, i.e., task offloading of MEC, traffic scheduling of SDN, and performance analysis of physical training, and the results show that the proposed MSPT has better performance than the corresponding baselines.

scholarly journals Container-Based Fast Service Migration Method for Mobile Edge Computing

2021 ◽  
Author(s):  
Xianyu Meng ◽  
Wei Lu
Keyword(s):  
Dynamic Loading ◽  
File System ◽  
High Reliability ◽  
Rapid Development ◽  
Edge Computing ◽  
Migration Time ◽  
Mobile Edge Computing ◽  
Computing Center ◽  
Service Migration ◽  
Iot Devices

Mobile edge computing (MEC) provides users with low-latency, high-bandwidth, and high-reliability services by migrating the computing power of the cloud computing center to the edge of the network. It is thus being considered an effective solution for the contradiction between the limited computing capabilities of Internet of Things (IoT) devices and the rapid development of delay-sensitive real-time applications. In this study, we propose and design a container union file system based on the differencing hard disk and dynamic loading strategy to address the excessively long migration time caused by the bundling transmission of the file system and container images during container-based service migration. The proposed method involves designing a mechanism based on a remote dynamic loading strategy to avoid the downloading of all container images, thereby reducing the long preparation time before which stateless migration can begin. Furthermore, in view of the excessive latency of the edge service during the stateful migration process, a strategy for avoiding the transmission of the underlying file system and container images is designed to optimize the service interruption time and service quality degradation time. Experiments show that the proposed method and strategy can effectively reduce the migration time of container-based services.

Optimal Offloading for Streaming Applications in Mobile Edge Computing

2021 ◽  
Author(s):  
Pengfei Sun ◽  
Xue-Yang Zhu ◽  
Ya Gao
Keyword(s):  
Mobile Devices ◽  
Rapid Development ◽  
Heuristic Method ◽  
Exact Algorithm ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Computationally Efficient ◽  
Streaming Applications ◽  
Smart Mobile

With the rapid development of smart mobile devices, mobile applications are becoming more and more popular. Since mobile devices usually have constrained computing capacity, computation offloading to mobile edge computing (MEC) to achieve a lower latency is a promising paradigm. In this paper, we focus on the optimal offloading problem for streaming applications in MEC. We present solutions to find offloading policies of streaming applications to achieve an optimal latency. Streaming applications are modeled with synchronous data flow graphs. Two architecture assumptions are considered — with sufficient processors on both the local device and the MEC server, and with a limited number of processors on both sides. The problem is generally NP-complete. We present an exact algorithm and a heuristic algorithm for the former architecture assumption and a heuristic method for the latter. We carry out our experiments on a practical application and thousands of synthetic graphs to comprehensively evaluate our methods. The experimental results show that our methods are effective and computationally efficient.

scholarly journals A user interest community evolution model based on subgraph matching for social networking in mobile edge computing environments

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Liang Jiang ◽  
Lu Liu ◽  
Jingjing Yao ◽  
Leilei Shi
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Fundamental Problem ◽  
Rapid Development ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
User Interest ◽  
Subgraph Matching ◽  
Model Based ◽  
Community Evolution

AbstractWith the rapid development of mobile edge computing, mobile social networks are gradually infiltrating into our daily lives, in which the communities are an important part of social networks. Internet of People such as online social networks is the next frontier for the Internet of Things. The combination of social networking and mobile edge computing has an important application value and is the development trend of future networks. However, how to detect evolutionary communities accurately and efficiently in dynamic heterogeneous social networks remains a fundamental problem. In this paper, a novel User Interest Community Evolution (UICE) model based on subgraph matching is proposed for accurately detecting the corresponding communities in the evolution of the user interest community. The community evolutionary events can be quickly captured including forming, dissolving, evolving and so on with the introduction of core subgraph. A variant of subgraph matching, called Subgraph Matching with Dynamic Weight (SMDW), is proposed to solve the problem of updating the core subgraph due to the change of core user’s interest when tracking evolutionary communities. Finally, the experiments based on the real datasets have been designed to evaluate the performance of the proposed model by comparing it with the state-of-art methods in this area and complete data processing through the local edge computing layer. The experimental results demonstrate that the UICE model presented in this paper has achieved better accuracy, higher efficiency and better scalability against existing methods.

scholarly journals Virtual Machine Migration Strategy Based on Multi-Agent Deep Reinforcement Learning

2021 ◽  
Vol 11 (17) ◽  
pp. 7993
Author(s):  
Yu Dai ◽  
Qiuhong Zhang ◽  
Lei Yang
Keyword(s):  
Reinforcement Learning ◽  
Virtual Machine ◽  
Control Method ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Centralized Control ◽  
Virtual Machine Migration ◽  
Migration Strategy ◽  
Computing Power ◽  
Multi Agent

Mobile edge computing is a new computing model, which pushes cloud computing power from centralized cloud to network edge. However, with the sinking of computing power, user mobility brings new challenges: since it is usually unstable, services should be dynamically migrated between multiple edge servers to maintain service performance, that is, user-perceived latency. Considering that Mobile Edge Computing is a highly distributed computing environment and it is difficult to synchronize information between servers, in order to ensure the real-time performance of the migration strategy, a virtual machine migration strategy based on Multi-Agent Deep Reinforcement Learning is proposed in this paper. The method of centralized training and distributed execution is adopted, that is, the transfer action is guided by the global information during training, and only the local observation information is needed to obtain the transfer action. Compared with the centralized control method, the proposed method alleviates communication bottleneck. Compared with other distributed control methods, this method only needs local information, does not need communication between servers, and speeds up the perception of the current environment. Migration strategies can be generated faster. Simulation results show that the proposed strategy is better than the contrast strategy in terms of convergence and energy consumption.

scholarly journals A Zero-Proof Knowledge Based on NFC for Data Authentication/Protection using Blockchain for Mobile Edge Computing

2020 ◽  
Vol 8 (6) ◽  
pp. 2547-2552
Keyword(s):  
Web Application ◽  
Rapid Development ◽  
Edge Computing ◽  
The Internet ◽  
Mobile Edge Computing ◽  
Everyday Objects ◽  
Knowledge Based ◽  
Data Authentication ◽  
Proposed Model ◽  
The Internet Of Things

Mobile edge computing is a recent trend to complement the Internet of Things (IoT) ecosystem in the computing sector. IoT is the internet connected communications related to physical devices and everyday objects. The emergence of intelligent living spaces has been due to the rapid development of IoT technologies. Blockchain is one such technology that expands the list of information also referred to like records that are saved as blocks in the Blockchain which are connected using cryptographic algorithms. Within a Blockchain IoT environment, when data or device authentication information is stored in a Blockchain, authentication information can be displayed when verifying Block chain’s transactions, which are also referred to as proof of work. A principle of Zero-knowledge proof (ZKF) is implemented in this paper which is a way of proving that knowledge is known without exposing any data to the user. The proposed model uses a Mobile application where users can prove without revealing users' passwords. Blockchain stores client information that can prevent data from being manipulated. The results of applying the ZKF theory for data security are shown through a web application and NFC.

scholarly journals Homomorphic Evaluation of the Integer Arithmetic Operations for Mobile Edge Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Changqing Gong ◽  
Mengfei Li ◽  
Liang Zhao ◽  
Zhenzhou Guo ◽  
Guangjie Han
Keyword(s):  
Cloud Computing ◽  
Homomorphic Encryption ◽  
Rapid Development ◽  
Heterogeneous Data ◽  
Edge Computing ◽  
Security And Privacy ◽  
Mobile Edge Computing ◽  
Operation Rules ◽  
Original Scheme ◽  
Time Overhead

With the rapid development of the 5G network and Internet of Things (IoT), lots of mobile and IoT devices generate massive amounts of multisource heterogeneous data. Effective processing of such data becomes an urgent problem. However, traditional centralised models of cloud computing are challenging to process multisource heterogeneous data effectively. Mobile edge computing (MEC) emerges as a new technology to optimise applications or cloud computing systems. However, the features of MEC such as content perception, real-time computing, and parallel processing make the data security and privacy issues that exist in the cloud computing environment more prominent. Protecting sensitive data through traditional encryption is a very secure method, but this will make it impossible for the MEC to calculate the encrypted data. The fully homomorphic encryption (FHE) overcomes this limitation. FHE can be used to compute ciphertext directly. Therefore, we propose a ciphertext arithmetic operation that implements data with integer homomorphic encryption to ensure data privacy and computability. Our scheme refers to the integer operation rules of complement, addition, subtraction, multiplication, and division. First, we use Boolean polynomials (BP) of containing logical AND, XOR operations to represent the rulers. Second, we convert the BP into homomorphic polynomials (HP) to perform ciphertext operations. Then, we optimise our scheme. We divide the ciphertext vector of integer encryption into subvectors of length 2 and increase the length of private key of FHE to support the 3-multiplication level additional. We test our optimised scheme in DGHV and CMNT. In the number of ciphertext refreshes, the optimised scheme is reduced by 2/3 compared to the original scheme, and the time overhead of our scheme is reduced by 1/3. We also examine our scheme in CNT of without bootstrapping. The time overhead of optimised scheme over DGHV and CMNT is close to the original scheme over CNT.

scholarly journals A mobile edge computing/software‐defined networking‐enabled architecture for vehicular networks

2019 ◽  
Vol 3 (6) ◽  
Author(s):  
Ammar Muthanna ◽  
Regina Shamilova ◽  
Abdelhamied A. Ateya ◽  
Alexander Paramonov ◽  
Mohammad Hammoudeh
Keyword(s):  
Vehicular Networks ◽  
Software Defined Networking ◽  
Edge Computing ◽  
Mobile Edge Computing

scholarly journals Deep Reinforcement Learning-Empowered Resource Allocation for Mobile Edge Computing in Cellular V2X Networks

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 372
Author(s):  
Dongji Li ◽  
Shaoyi Xu ◽  
Pengyu Li
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Vehicular Networks ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Dynamic Environment ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Computing Paradigm ◽  
Cloud Servers

With the rapid development of vehicular networks, vehicle-to-everything (V2X) communications have huge number of tasks to be calculated, which brings challenges to the scarce network resources. Cloud servers can alleviate the terrible situation regarding the lack of computing abilities of vehicular user equipment (VUE), but the limited resources, the dynamic environment of vehicles, and the long distances between the cloud servers and VUE induce some potential issues, such as extra communication delay and energy consumption. Fortunately, mobile edge computing (MEC), a promising computing paradigm, can ameliorate the above problems by enhancing the computing abilities of VUE through allocating the computational resources to VUE. In this paper, we propose a joint optimization algorithm based on a deep reinforcement learning algorithm named the double deep Q network (double DQN) to minimize the cost constituted of energy consumption, the latency of computation, and communication with the proper policy. The proposed algorithm is more suitable for dynamic scenarios and requires low-latency vehicular scenarios in the real world. Compared with other reinforcement learning algorithms, the algorithm we proposed algorithm improve the performance in terms of convergence, defined cost, and speed by around 30%, 15%, and 17%.

scholarly journals Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing

2021 ◽  
Author(s):  
Yao Du ◽  
Shuxiao Miao ◽  
Zitian Tong ◽  
Victoria Lemieux ◽  
Zehua Wang
Keyword(s):  
Machine Learning ◽  
Data Sharing ◽  
Research Field ◽  
Edge Computing ◽  
Training Data ◽  
Learning Approaches ◽  
Mobile Edge Computing ◽  
User Privacy ◽  
Secure Data ◽  
Iot Devices

Driven by recent advancements in machine learning, mobile edge computing (MEC) and the Internet of things (IoT), artificial intelligence (AI) has become an emerging technology. Traditional machine learning approaches require the training data to be collected and processed in centralized servers. With the advent of new decentralized machine learning approaches and mobile edge computing, the IoT on-device data training has now become possible. To realize AI at the edge of the network, IoT devices can offload training tasks to MEC servers. However, those distributed frameworks of edge intelligence also introduce some new challenges, such as user privacy and data security. To handle these problems, blockchain has been considered as a promising solution. As a distributed smart ledger, blockchain is renowned for high scalability, privacy-preserving, and decentralization. This technology is also featured with automated script execution and immutable data records in a trusted manner. In recent years, as quantum computers become more and more promising, blockchain is also facing potential threats from quantum algorithms. In this chapter, we provide an overview of the current state-of-the-art in these cutting-edge technologies by summarizing the available literature in the research field of blockchain-based MEC, machine learning, secure data sharing, and basic introduction of post-quantum blockchain. We also discuss the real-world use cases and outline the challenges of blockchain-empowered intelligence.

scholarly journals Visitor Information System of Cross-Border E-Commerce Platform Based on Mobile Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaheng Zhang ◽  
Yonghua Cai ◽  
Lin Xiao
Keyword(s):  
Information System ◽  
Rapid Development ◽  
Edge Computing ◽  
Pass Rate ◽  
Mobile Edge Computing ◽  
Information Analysis ◽  
Cross Border ◽  
The Cross ◽  
Analysis System ◽  
Computing Method

With the popularity of the Internet and the rapid development of e-commerce, online shopping has gradually become an indispensable part of people’s lives. Among them, the rise of cross-border e-commerce has become a focus of attention. The operation traces left by visitors during shopping on the e-commerce platform are stored in the database of the system, and the platform holds such a large amount of valuable data resources. How to unearth valuable content from these resources and apply them becomes very important. This article mainly introduces the research on the visitor information analysis system of the cross-border e-commerce platform based on mobile edge computing. This article first establishes the mobile edge computing framework based on the advantages of the mobile edge computing method and uses it to visit visitors in the visitor information analysis system. In the data filtering, secondly, the requirements of the visitor information analysis system of the cross-border e-commerce platform are analyzed to provide a design basis for the design of the visitor information system. Finally, the visitor information analysis based on the mobile edge algorithm is designed through the demand analysis of the system that has also been tested for visitor information analysis. The test pass rate is as high as 98%, and the accuracy rate of visitor information analysis reaches 80%.

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