scholarly journals Application of Mobile Edge Computing Technology in Civil Aviation Express Marketing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ying Yu
Keyword(s):  
Data Processing ◽  
Clustering Algorithm ◽  
Rapid Development ◽  
Base Station ◽  
Edge Computing ◽  
Civil Aviation ◽  
Mobile Edge Computing ◽  
Core Network ◽  
Computing Technology ◽  
Processing And Storage

With the popularization of mobile terminals and the rapid development of mobile communication technology, many PC-based services have placed high demands on data processing and storage functions. Cloud laptops that transfer data processing tasks to the cloud cannot meet the needs of users due to low latency and high-quality services. In view of this, some researchers have proposed the concept of mobile edge computing. Mobile edge computing (MEC) is based on the 5G evolution architecture. By deploying multiple service servers on the base station side near the edge of the user’s mobile core network, it provides nearby computing and processing services for user business. This article is aimed at studying the use of caching and MEC processing functions to design an effective caching and distribution mechanism across the network edge and apply it to civil aviation express marketing. This paper proposes to focus on mobile edge computing technology, combining it with data warehouse technology, clustering algorithm, and other methods to build an experimental model of MEC-based caching mechanism applied to civil aviation express marketing. The experimental results in this paper show that when the cache space and the number of service contents are constant, the LECC mechanism among the five cache mechanisms is more effective than LENC, LRU, and RR in cache hit rate, average content transmission delay, and transmission overhead. For example, with the same cache space, ATC under the LECC mechanism is about 4%~9%, 8%~13%, and 18%~22% lower than that of LENC, LRU, and RR, respectively.

scholarly journals Dynamic Task Migration Combining Energy Efficiency and Load Balancing Optimization in Three-Tier UAV-Enabled Mobile Edge Computing System

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 190
Author(s):  
Wu Ouyang ◽  
Zhigang Chen ◽  
Jia Wu ◽  
Genghua Yu ◽  
Heng Zhang
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Markov Decision Process ◽  
Decision Process ◽  
Base Station ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Task Migration ◽  
Migration Strategy ◽  
Markov Decision

As transportation becomes more convenient and efficient, users move faster and faster. When a user leaves the service range of the original edge server, the original edge server needs to migrate the tasks offloaded by the user to other edge servers. An effective task migration strategy needs to fully consider the location of users, the load status of edge servers, and energy consumption, which make designing an effective task migration strategy a challenge. In this paper, we innovatively proposed a mobile edge computing (MEC) system architecture consisting of multiple smart mobile devices (SMDs), multiple unmanned aerial vehicle (UAV), and a base station (BS). Moreover, we establish the model of the Markov decision process with unknown rewards (MDPUR) based on the traditional Markov decision process (MDP), which comprehensively considers the three aspects of the migration distance, the residual energy status of the UAVs, and the load status of the UAVs. Based on the MDPUR model, we propose a advantage-based value iteration (ABVI) algorithm to obtain the effective task migration strategy, which can help the UAV group to achieve load balancing and reduce the total energy consumption of the UAV group under the premise of ensuring user service quality. Finally, the results of simulation experiments show that the ABVI algorithm is effective. In particular, the ABVI algorithm has better performance than the traditional value iterative algorithm. And in a dynamic environment, the ABVI algorithm is also very robust.

Design and Implement of Data Acquisition System Based on Reconfigurable Computing

2012 ◽  
Vol 546-547 ◽  
pp. 1393-1397
Author(s):  
Zhi Wen Xiong ◽  
Chen Guang Xu ◽  
Hong Zeng
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Reconfigurable Computing ◽  
Rapid Development ◽  
Physical Property ◽  
Data Acquisition System ◽  
Digital System ◽  
Working Environment ◽  
Acquisition System ◽  
Computing Technology

Data acquisition begins with the physical phenomenon or physical property to be measured. Examples of this include temperature, gas pressure, and light intensity, and force, fluid flow, regardless of the type of physical property to be measured. Physical property converted into digital, and then by the computer for storage, processing, display or printing process, the corresponding system is called data acquisition system. With the rapid development of computer technology, data acquisition systems quickly gained popularity. A variety of products based on digital technology have been created. Digital System spread quickly; it’s mainly the following two advantages: the first is the digital processing flexible and convenient; the second is a digital system is very reliable. The main idea of Reconfigurable computing technology [1] is using the FPGA [2][3] allows the system has a dynamically configurable capacity, suitable for harsh environment applications, improve the speed of data processing. By the use of dynamic reconfigurable FPGA devices can be realized on the hardware logic function modification, application of reconfigurable computing technology can improve the speed of data processing. Data acquisition system is widely applied in many fields, and often used the abominable working environment place. The reconfigurable computing technology, can greatly improve the data acquisition system reliability and safety. The paper introduces a kind of multi-channel data acquisition system based on USB bus and FPGA, the factors affecting the performance of system are discussed, and describes how to use reconfigurable computing technology to improve the efficiency of data acquisition system while reduce energy consumption. The system in this paper uses AD's AD9220, ALTERA's EP1C6-8 and IDT's IDT70V24, Cypress’s CY7C68013.

scholarly journals Mobility-Aware Service Caching in Mobile Edge Computing for Internet of Things

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 610 ◽  
Author(s):  
Hua Wei ◽  
Hong Luo ◽  
Yan Sun
Keyword(s):  
Target Location ◽  
Geometric Model ◽  
Back Propagation ◽  
Base Station ◽  
Edge Computing ◽  
Computation Offloading ◽  
Target Area ◽  
Mobile Edge Computing ◽  
Trajectory Data ◽  
Practical Applications

The mobile edge computing architecture successfully solves the problem of high latency in cloud computing. However, current research focuses on computation offloading and lacks research on service caching issues. To solve the service caching problem, especially for scenarios with high mobility in the Sensor Networks environment, we study the mobility-aware service caching mechanism. Our goal is to maximize the number of users who are served by the local edge-cloud, and we need to make predictions about the user’s target location to avoid invalid service requests. First, we propose an idealized geometric model to predict the target area of a user’s movement. Since it is difficult to obtain all the data needed by the model in practical applications, we use frequent patterns to mine local moving track information. Then, by using the results of the trajectory data mining and the proposed geometric model, we make predictions about the user’s target location. Based on the prediction result and existing service cache, the service request is forwarded to the appropriate base station through the service allocation algorithm. Finally, to be able to train and predict the most popular services online, we propose a service cache selection algorithm based on back-propagation (BP) neural network. The simulation experiments show that our service cache algorithm reduces the service response time by about 13.21% on average compared to other algorithms, and increases the local service proportion by about 15.19% on average compared to the algorithm without mobility prediction.

scholarly journals An Intelligent Offloading System Based on Multiagent Reinforcement Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yu Weng ◽  
Haozhen Chu ◽  
Zhaoyi Shi
Keyword(s):  
Reinforcement Learning ◽  
Base Station ◽  
Intelligent Vehicles ◽  
Edge Computing ◽  
Core Network ◽  
Resource Requirement ◽  
Node Scheduling ◽  
Multiagent Reinforcement Learning ◽  
Selection Of

Intelligent vehicles have provided a variety of services; there is still a great challenge to execute some computing-intensive applications. Edge computing can provide plenty of computing resources for intelligent vehicles, because it offloads complex services from the base station (BS) to the edge computing nodes. Before the selection of the computing node for services, it is necessary to clarify the resource requirement of vehicles, the user mobility, and the situation of the mobile core network; they will affect the users’ quality of experience (QoE). To maximize the QoE, we use multiagent reinforcement learning to build an intelligent offloading system; we divide this goal into two suboptimization problems; they include global node scheduling and independent exploration of agents. We apply the improved Kuhn–Munkres (KM) algorithm to node scheduling and make full use of existing edge computing nodes; meanwhile, we guide intelligent vehicles to the potential areas of idle computing nodes; it can encourage their autonomous exploration. Finally, we make some performance evaluations to illustrate the effectiveness of our constructed system on the simulated dataset.

scholarly journals Distributed Edge Computing to Assist Ultra Low Latency VANET Applications

2018 ◽  
Author(s):  
Andrei Vladyko ◽  
Abdukodir Khakimov ◽  
Ammar Muthanna ◽  
Abdelhamied A. Ateya ◽  
Andrey Koucheryavy
Keyword(s):  
Base Station ◽  
Road Accident ◽  
Edge Computing ◽  
Transport Infrastructure ◽  
City Management ◽  
Mobile Edge Computing ◽  
General Will ◽  
Provision Of Services ◽  
Stationary Objects ◽  
Roadside Unit

VANET networks are a class of peer-to-peer wireless networks that are used to organize communication between cars (V2V), cars and infrastructure (V2I) and between cars and other types of nodes (V2X). These networks are based on the DSRC, 802.11 standards and are mainly intended for organizing the exchange of various types of messages, mainly emergency ones, to prevent road accidents or alert when road accident occur, or control the priority of the driveway. Initially it was assumed that cars would only interact with each other, but later, with the advent of the concept of Internet of things (IoT). Researchers began to analyze connectivity with other devices, which in general will allow to combine various road users and other devices that can used in the creation of intelligent transport infrastructure in a single smart city management system. Infrastructure is necessary for the provision of services, monitoring and management of the VANET network. As infrastructure objects it is proposed to use stationary objects of Roadside unit (RSU). The aim of this paper is to analyze the use of mobile edge computing to decrease the load to the base station and latency between RSU clouds and provide a real experiment using software defined networking and mobile edge computing for RSU.

scholarly journals Computational off loading through 5G Enabled Edge Computing in IIOT

2020 ◽  
Vol 8 (6) ◽  
pp. 1417-1420
Keyword(s):  
Game Theory ◽  
Rapid Development ◽  
Radio Spectrum ◽  
Base Station ◽  
Edge Computing ◽  
User Equipment ◽  
Theory Approach ◽  
Computational Offloading ◽  
Active Research ◽  
Iot Devices

Computational offloading is the active research nowadays. To improve the computational offloading and security of data we use game theory approach and 5G enabled edge computing. Edge computing is providing solution across various sectors Such arrangements not just decreases load on the cloud by preparing information on the edge, yet in addition assume a significant job in information security by guaranteeing information correspondence is privately meant system which legitimately interfaces the user equipment, at that point the nearby server sends to the organization’s network core. With organizations and other institutes looking to integrate this edge-focused approach to their communication infrastructure, it is used in collection and managing data securely. This with a combination of 5G and game theory approach we can easily manage the data transmission. Game theory approach helps IoT devices to take decision autonomously and reduce computational offloading.5G is on the rapid development than ever before. Due to its super-fast speeds, high bandwidth, reduced latency and increased capacity over 4G, 5G has the ability to provide greater security and computational offloading than 4G. The 5G network’s speed likely to reach 20-30 times faster than what the 4G network allows. That improvement opens possibilities for far-away sensors to connect and reduce latency through local servers. 5G uses distributed network of base station in small cell infrastructure because of its shorter range. Due to its higher frequency 5G uses new radio spectrum (N-RAM) structure.5G further improves the data security by using network slicing. Thus our experiment in this paper use dynamic computational offloading algorithm to the user equipment which transfer data via 5G.

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 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.

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