Tourism scene monitoring using mobile edge computing and business driven action learning. (14th July 2022)

• Record Type: Journal Article
• Authors: Yu, Hong
• Is Part Of: International journal of grid and utility computing. Volume 13:Number 2/3(2022)
• Page Start: 282
• Page End: 290
• Publication Date: 2022-07-14
• Subjects: edge computing -- business-driven action learning -- data analysis -- tourism management -- scene monitoring; Electronic data processing -- Distributed processing -- Periodicals; Electronic commerce -- Management -- Computer programs -- Periodicals; 004.605
• Languages: English
• ISSNs: 1741-847X

Abstract

In order to improve the environment of domestic tourism scenes, enforce its monitoring means and strength, and improve its ability to respond to emergencies, the monitoring means of domestic tourism scenes under big data is discussed based on Mobile Edge Computing (MEC) and Business Driven Action Learning (BDAL). First, the concept and construction of edge computing are analysed, including tourist number monitoring, tourist density and the security of tourist scenes. And these monitoring data are processed in time to make a reasonable tourist diversion scheme; second, the BDAL is analysed to explore its function of data analysis and processing; finally, a tourism scene monitoring modal is constructed.