{"title":"Smart Data Collection in Mobile Edge Computing Environment","authors":"I. Tikito, N. Souissi","doi":"10.1109/ISCV49265.2020.9204277","DOIUrl":null,"url":null,"abstract":"With the digital transformation, businesses and public administrations must change the place of data in the value chain to serve all areas of the business and open up information systems. The value of the knowledge extracted from this data is directly linked to the quality of data collection. Mobile devices are particularly suitable for reporting data. They are very widespread, very suitable and can be used at any time. These characteristics mean that the use of mobile support for data collection corresponds to a paradigm shift more than a simple new additional technology compared to the panoply of existing tools. The explosion of information sharing and data, which stems from our daily by these devices is stored mostly in the cloud servers. Thus, to reduce the number of data transferred and generated by mobile devices to the cloud servers, the edge computing allows to process data at the network edge where they are generated directly reducing certain characteristics of Big Data. Big data involves the collection of complex data on the “V” dimensions which describe the quantity and type of data collected, as well as their importance and relevance to the challenges of the requester. However, the smart data goes a step further and consist to extract from the data collected only the most relevant information for the client in order to make predictions. Our results show that using an intelligent data collection process in mobile computing could generate savings in terms of data storage and analysis at the cloud level.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCV49265.2020.9204277","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
With the digital transformation, businesses and public administrations must change the place of data in the value chain to serve all areas of the business and open up information systems. The value of the knowledge extracted from this data is directly linked to the quality of data collection. Mobile devices are particularly suitable for reporting data. They are very widespread, very suitable and can be used at any time. These characteristics mean that the use of mobile support for data collection corresponds to a paradigm shift more than a simple new additional technology compared to the panoply of existing tools. The explosion of information sharing and data, which stems from our daily by these devices is stored mostly in the cloud servers. Thus, to reduce the number of data transferred and generated by mobile devices to the cloud servers, the edge computing allows to process data at the network edge where they are generated directly reducing certain characteristics of Big Data. Big data involves the collection of complex data on the “V” dimensions which describe the quantity and type of data collected, as well as their importance and relevance to the challenges of the requester. However, the smart data goes a step further and consist to extract from the data collected only the most relevant information for the client in order to make predictions. Our results show that using an intelligent data collection process in mobile computing could generate savings in terms of data storage and analysis at the cloud level.