edgecomputing is one of the key driving forces to enable beyond 5G (B5G) and 6G networks. To cope with the unprecedented increase in traffic volumes and computation demands of future networks, multiaccess (or mobile)...
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edgecomputing is one of the key driving forces to enable beyond 5G (B5G) and 6G networks. To cope with the unprecedented increase in traffic volumes and computation demands of future networks, multiaccess (or mobile) edgecomputing (MEC) is considered a promising solution that provides cloud computing capabilities within the radio access network. There exists a significant amount of research on MEC and its potential applications;however, very little has been said about the key factors of MEC deployment to meet the diverse demands of future applications. This article presents key considerations for edge deployments in B5G/6G networks, including edge architecture, server location, capacity, user density, security, etc. We further present state-of-the-art edge-centric services in future B5G/6G networks, including experimental evaluation of edge-based computing. Lastly, we present some interesting insights and open research problems in edgecomputing for 6G networks.
With the development of "Internet plus" and the deep integration of information and network based technology and network technology, Industry 4.0 has become a hot topic. Industrial production is faced with a...
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With the development of "Internet plus" and the deep integration of information and network based technology and network technology, Industry 4.0 has become a hot topic. Industrial production is faced with a large number of tasks, complex and changeable demands, and difficult to predict. In order to solve a series of objective problems such as task delay in actual production, this paper proposed a method based on edgecomputing (EC) to reduce the delay to meet the real-time requirements of industrial production. However, due to the limitation of its computing power and storage capacity, it is difficult to adapt to large-scale data decision-making. In terms of the lag rate of the algorithm in the experiment of the intelligent decision model, when the number of tasks of EC algorithm was 15 and 6, the lag rate was the highest and the lowest, and its values were 16 % and 2 % respectively. Therefore, it can be seen that EC algorithm can play a good role in the intelligent management and decision-making model of operational research.
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