scholarly journals Application of Basketball Training System Based on Dynamic Intelligent Fog Computing Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ke Yang
Keyword(s):  
Genetic Algorithm ◽  
Fog Computing ◽  
Academic Community ◽  
Training System ◽  
Mobile Internet ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Transmission Distance ◽  
Test Input ◽  
On The Road

Although the development of the mobile Internet and the Internet of Things has greatly promoted the progress and development of society, it has also created many problems for people on the road of scientific and technological exploration. In order to meet the problems and requirements of high bandwidth, high load, and low latency in the current network development, the emergence of the concept of mobile edge computing has attracted extensive attention from the academic community. This article focuses on the representative mode of mobile edge computing-fog computing (in this model, data, (data)processing, and applications are concentrated in devices at the edge of the network, instead of being stored almost entirely in the cloud). By applying it to the development and operation of basketball training system, it explores the performance of dynamic intelligent fog computing in intelligent end user services. This paper proposes a fog resource scheduling scheme based on linear weighted genetic algorithm, which converts the problem of multiobjective optimization into a single-objective optimization problem. When applying the genetic algorithm based on weighted sum, preference is given to delay, communication load, and service cost. Value is integrated into an objective function to perform genetic operations to get a better solution. From the experimental data, the system can support 20 DCTU terminals with a pressure request of 10 messages per second per terminal under the pressure environment created by the pressure test input data. The barrier-free transmission distance is 200 m, and the barrier transmission distance is 50 m. It has strong fault tolerance.

scholarly journals A New Edge Computing based Cloud System for IoT Applications

2019 ◽  
Vol 8 (2) ◽  
pp. 4289-4293
Keyword(s):  
Cloud Computing ◽  
Spectral Efficiency ◽  
Telecommunication Network ◽  
Fog Computing ◽  
Mobile Internet ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Novel Technology ◽  
Different Characteristics ◽  
The Internet Of Things

The mobile internet and the internet of things (IoT) has emerged out with various applications were centralized cloud computing has faced several challenges over the past years. Challenges include high latency and low Spectral Efficiency. Nevertheless, these challenges can be faced using a novel technology which is now emerging out as a major trending technology that supersedes centralized cloud computing with edge devices of networks. Well, this technology will reduce the latency and will enhance spectral efficiency and will also support massive machine types of communication. A detailed description of this trending technology deals with mobile edge computing, cloudlets and fog computing. In addition, the functioning process of each computing technology is also included. The different characteristics of mobile edge computing and fog computing have been focused. However the most significant part of how these technologies work under the discussion of telecommunication network is also briefly explained.

scholarly journals Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhuofan Liao ◽  
Jingsheng Peng ◽  
Bing Xiong ◽  
Jiawei Huang
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Cellular Networks ◽  
Physical Distance ◽  
Edge Computing ◽  
Base Stations ◽  
Mobile Edge Computing ◽  
Total Energy Consumption ◽  
Average Delay ◽  
Server Selection

AbstractWith the combination of Mobile Edge Computing (MEC) and the next generation cellular networks, computation requests from end devices can be offloaded promptly and accurately by edge servers equipped on Base Stations (BSs). However, due to the densified heterogeneous deployment of BSs, the end device may be covered by more than one BS, which brings new challenges for offloading decision, that is whether and where to offload computing tasks for low latency and energy cost. This paper formulates a multi-user-to-multi-servers (MUMS) edge computing problem in ultra-dense cellular networks. The MUMS problem is divided and conquered by two phases, which are server selection and offloading decision. For the server selection phases, mobile users are grouped to one BS considering both physical distance and workload. After the grouping, the original problem is divided into parallel multi-user-to-one-server offloading decision subproblems. To get fast and near-optimal solutions for these subproblems, a distributed offloading strategy based on a binary-coded genetic algorithm is designed to get an adaptive offloading decision. Convergence analysis of the genetic algorithm is given and extensive simulations show that the proposed strategy significantly reduces the average latency and energy consumption of mobile devices. Compared with the state-of-the-art offloading researches, our strategy reduces the average delay by 56% and total energy consumption by 14% in the ultra-dense cellular networks.

scholarly journals Computation Offloading Strategy in Mobile Edge Computing

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 191 ◽  
Author(s):  
Jinfang Sheng ◽  
Jie Hu ◽  
Xiaoyu Teng ◽  
Bin Wang ◽  
Xiaoxia Pan
Keyword(s):  
Decision Making ◽  
User Experience ◽  
Task Scheduling ◽  
Mobile Internet ◽  
Edge Computing ◽  
Computation Offloading ◽  
Smart Devices ◽  
Mobile Edge Computing ◽  
Network Bandwidth ◽  
Intelligent Devices

Mobile phone applications have been rapidly growing and emerging with the Internet of Things (IoT) applications in augmented reality, virtual reality, and ultra-clear video due to the development of mobile Internet services in the last three decades. These applications demand intensive computing to support data analysis, real-time video processing, and decision-making for optimizing the user experience. Mobile smart devices play a significant role in our daily life, and such an upward trend is continuous. Nevertheless, these devices suffer from limited resources such as CPU, memory, and energy. Computation offloading is a promising technique that can promote the lifetime and performance of smart devices by offloading local computation tasks to edge servers. In light of this situation, the strategy of computation offloading has been adopted to solve this problem. In this paper, we propose a computation offloading strategy under a scenario of multi-user and multi-mobile edge servers that considers the performance of intelligent devices and server resources. The strategy contains three main stages. In the offloading decision-making stage, the basis of offloading decision-making is put forward by considering the factors of computing task size, computing requirement, computing capacity of server, and network bandwidth. In the server selection stage, the candidate servers are evaluated comprehensively by multi-objective decision-making, and the appropriate servers are selected for the computation offloading. In the task scheduling stage, a task scheduling model based on the improved auction algorithm has been proposed by considering the time requirement of the computing tasks and the computing performance of the mobile edge computing server. Extensive simulations have demonstrated that the proposed computation offloading strategy could effectively reduce service delay and the energy consumption of intelligent devices, and improve user experience.

scholarly journals Task Scheduling for Mobile Edge Computing Using Genetic Algorithm and Conflict Graphs

2020 ◽  
Vol 69 (8) ◽  
pp. 8805-8819
Author(s):  
Ahmed A. Al-Habob ◽  
Octavia A. Dobre ◽  
Ana Garcia Armada ◽  
Sami Muhaidat
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Conflict Graphs

scholarly journals Genetic Algorithm-Based Optimization of Offloading and Resource Allocation in Mobile-Edge Computing

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 83 ◽  
Author(s):  
Zhi Li ◽  
Qi Zhu
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Completion Time ◽  
Access Network ◽  
Optimization Method ◽  
Edge Computing ◽  
Smart Devices ◽  
Task Completion ◽  
Mobile Edge Computing ◽  
Task Completion Time

Mobile edge computing (MEC) can use a wireless access network to serve smart devices nearby so as to improve the service experience of users. In this paper, a joint optimization method based on the Genetic Algorithm (GA) for task offloading proportion, channel bandwidth, and mobile edge servers’ (MES) computing resources is proposed in the scenario where some computing tasks can be partly offloaded to the MES. Under the limitation of wireless transmission resources and MESs’ processing resources, GA was used to solve the optimization problem of minimizing user task completion time, and the optimal offloading task strategy and resource allocation scheme were obtained. The simulation results show that the proposed algorithm can effectively reduce the task completion time and ensure the fairness of users’ completion times.

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