Animation Art Design Online System Based on Mobile Edge Computing and User Perception

Based on mobile edge computing and user perception technology, this paper analyzes and discusses the respective advantages and disadvantages of the important optimization models and mobile models in the animation art design, as well as the wireless block data transmission mechanism and protocol. In order to solve the problem that user mobility cannot be sensed, a content-centric mobile edge animation art design mechanism based on user mobility perception is proposed. This mechanism comprehensively calculates the centrality of users’ perception of nodes, the idle rate of animation design, and the staying time of users in a small area. The mobile edge network controller integrates the information of each edge user’s perception node, calculates the importance of each edge user’s perception node and prioritizes it, and selects the appropriate content animation to design the user perception node according to the ranking result. Finally, various simulation or platform test experiments were carried out for all the design schemes in this paper, and the experimental results were analyzed. The simulation experiment results show that compared with the traditional animation design mechanism, the animation art design system effectively reduces the average number of hops for users to obtain content by up to 15.9%, improves the hit rate of edge user perception node animation design by at least 13.7%, and reduces the traffic entering the core network by up to 32.1%. According to the comparison results, the various designs in this work can successfully use sensor data to preclassify migration tasks in the mobile edge network environment. Compared with the latest block data transmission protocol, it has a significant performance improvement, reducing the data distribution delay by 34.8%, thereby helping to improve the overall efficiency of mobile edge computing.

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Tolerable Data Transmission of Mobile Edge Computing Under Internet of Things

IEEE Access ◽

10.1109/access.2019.2920442 ◽

2019 ◽

Vol 7 ◽

pp. 71859-71871

Author(s):

Jianwei Liu ◽

Xianglin Wei ◽

Jianhua Fan

Keyword(s):

Internet Of Things ◽

Data Transmission ◽

Edge Computing ◽

Mobile Edge Computing

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Resource management for mobile edge computing using user mobility prediction

2018 International Conference on Information Networking (ICOIN) ◽

10.1109/icoin.2018.8343212 ◽

2018 ◽

Cited By ~ 4

Author(s):

Takayuki Ojima ◽

Takeo Fujii

Keyword(s):

Resource Management ◽

Edge Computing ◽

Mobility Prediction ◽

User Mobility ◽

Mobile Edge Computing

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Physical Layer Secure Data Transmission for Mobile Edge Computing: Beamforming and Artificial Noise

Journal of Physics Conference Series ◽

10.1088/1742-6596/1659/1/012016 ◽

2020 ◽

Vol 1659 ◽

pp. 012016

Author(s):

Aidong Xu ◽

Jie Tang ◽

Yonggang Zeng ◽

Li Cheng Li ◽

Yixin Jiang ◽

...

Keyword(s):

Data Transmission ◽

Physical Layer ◽

Edge Computing ◽

Artificial Noise ◽

Mobile Edge Computing ◽

Secure Data ◽

Secure Data Transmission

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A dynamic service migration strategy based on mobility prediction in edge computing

International Journal of Distributed Sensor Networks ◽

10.1177/1550147721993403 ◽

2021 ◽

Vol 17 (2) ◽

pp. 155014772199340

Author(s):

Lanlan Rui ◽

Shuyun Wang ◽

Zhili Wang ◽

Ao Xiong ◽

Huiyong Liu

Keyword(s):

Optimization Method ◽

Edge Computing ◽

User Mobility ◽

Mobile Edge Computing ◽

Frequent User ◽

Migration Strategy ◽

Service Interruption ◽

Cost Constraints ◽

Initial Location ◽

Service Migration

Mobile edge computing is a new computing paradigm, which pushes cloud computing capabilities away from the centralized cloud to the network edge to satisfy the increasing amounts of low-latency tasks. However, challenges such as service interruption caused by user mobility occur. In order to address this problem, in this article, we first propose a multiple service placement algorithm, which initializes the placement of each service according to the user’s initial location and their service requests. Furthermore, we build a network model and propose a based on Lyapunov optimization method with long-term cost constraints. Considering the importance of user mobility, we use the Kalman filter to correct the user’s location to improve the success rate of communication between the device and the server. Compared with the traditional scheme, extensive simulation results show that the dynamic service migration strategy can effectively improve the service efficiency of mobile edge computing in the user’s mobile scene, reduce the delay of requesting terminal nodes, and reduce the service interruption caused by frequent user movement.

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Modified reinforcement learning based- caching system for mobile edge computing

Intelligent Decision Technologies ◽

10.3233/idt-190152 ◽

2020 ◽

pp. 1-16

Author(s):

Sarra Mehamel ◽

Samia Bouzefrane ◽

Soumya Banarjee ◽

Mehammed Daoui ◽

Valentina E. Balas

Keyword(s):

Reinforcement Learning ◽

Mobile Networks ◽

Mobile Network ◽

Edge Computing ◽

Mobile Edge Computing ◽

Network Nodes ◽

Caching Strategy ◽

Edge Network ◽

The Right ◽

And Storage

Caching contents at the edge of mobile networks is an efficient mechanism that can alleviate the backhaul links load and reduce the transmission delay. For this purpose, choosing an adequate caching strategy becomes an important issue. Recently, the tremendous growth of Mobile Edge Computing (MEC) empowers the edge network nodes with more computation capabilities and storage capabilities, allowing the execution of resource-intensive tasks within the mobile network edges such as running artificial intelligence (AI) algorithms. Exploiting users context information intelligently makes it possible to design an intelligent context-aware mobile edge caching. To maximize the caching performance, the suitable methodology is to consider both context awareness and intelligence so that the caching strategy is aware of the environment while caching the appropriate content by making the right decision. Inspired by the success of reinforcement learning (RL) that uses agents to deal with decision making problems, we present a modified reinforcement learning (mRL) to cache contents in the network edges. Our proposed solution aims to maximize the cache hit rate and requires a multi awareness of the influencing factors on cache performance. The modified RL differs from other RL algorithms in the learning rate that uses the method of stochastic gradient decent (SGD) beside taking advantage of learning using the optimal caching decision obtained from fuzzy rules.

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Optimization of Cooperative Offloading Model with Cost Consideration in Mobile Edge Computing

10.21203/rs.3.rs-165316/v1 ◽

2022 ◽

Author(s):

Bin Xu ◽

Tao Deng ◽

Yichuan Liu ◽

Yunkai Zhao ◽

Zipeng Xu ◽

...

Keyword(s):

Energy Consumption ◽

Mobile Devices ◽

Simulated Annealing Algorithm ◽

Minimum Energy ◽

Edge Computing ◽

Computation Offloading ◽

Neighborhood Search ◽

Mobile Edge Computing ◽

Edge Network ◽

Computing Capacity

Abstract The combination of idle computing resources in mobile devices and the computing capacity of mobile edge servers enables all available devices in an edge network to complete all computing tasks in coordination to effectively improve the computing capacity of the edge network. This is a research hotspot for 5G technology applications. Previous research has focused on the minimum energy consumption and/or delay to determine the formulation of the computational offloading strategy but neglected the cost required for the computation of collaborative devices (mobile devices, mobile edge servers, etc.); therefore, we proposed a cost-based collaborative computation offloading model. In this model, when a task requests these devices' assistance in computing, it needs to pay the corresponding calculation cost; and on this basis, the task is offloaded and computed. In addition, for the model, we propose an adaptive neighborhood search based on simulated annealing algorithm (ANSSA) to jointly optimize the offloading decision and resource allocation with the goal of minimizing the sum of both the energy consumption and calculation cost. The adaptive mechanism enables different operators to update the probability of selection according to historical experience and environmental perception, which makes the individual evolution have certain autonomy. A large number of experiments conducted on different scales of mobile user instances show that the ANSSA can obtain satisfactory time performance with guaranteed solution quality. The experimental results demonstrate the superiority of the mobile edge computing (MEC) offloading system. It is of great significance to strike a balance between maintaining the life cycle of smart mobile devices and breaking the performance bottleneck of MEC servers.

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