{"title":"利用云实现无人机生成数据的近实时处理","authors":"Sayani Sarkar, Michael W. Totaro, Khalid Elgazzar","doi":"10.1109/WIEForum47344.2019.8981670","DOIUrl":null,"url":null,"abstract":"Low-cost drones are an emerging technological area that open the horizon for intelligent new Internet-of-Things (IoT) and a host of other applications. Cloud-based online processing of unmanned aerial vehicle (UAV) captured data is an attractive and interesting option toward real-time data processing, as it accommodates viewing and analyzing data from a variety of sources, thereby making data accessibility across devices significantly smooth and easier. This results in a more effective and seamless professional collaborations. Cloud-based solutions also make information technology (IT) management easier by automatically installing software updates, storing backups and eliminating the need for high-specification and expensive computers for data processing and storage. The ability to upload more than one just data-set at a time also provides greater flexibility, while security matters are handled using log-in credentials. In the present work, we discuss a comparative study between two data processing approaches: in one case the data captured by an UAV has been processed using an on-board low-cost single-board computer; in the other case, the captured data has been off-loaded to the cloud for further processing. Processing times between these two approaches are compared, statistical analysis applied, in order to confirm the superiority of the cloud-based processing approach. Challenges inherent with cloud-based processing have also been identified, which will be the subject of future research work.","PeriodicalId":412628,"journal":{"name":"2019 IEEE Women in Engineering (WIE) Forum USA East","volume":"660 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Leveraging the Cloud to Achieve Near Real-time Processing for Drone-Generated Data\",\"authors\":\"Sayani Sarkar, Michael W. Totaro, Khalid Elgazzar\",\"doi\":\"10.1109/WIEForum47344.2019.8981670\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Low-cost drones are an emerging technological area that open the horizon for intelligent new Internet-of-Things (IoT) and a host of other applications. Cloud-based online processing of unmanned aerial vehicle (UAV) captured data is an attractive and interesting option toward real-time data processing, as it accommodates viewing and analyzing data from a variety of sources, thereby making data accessibility across devices significantly smooth and easier. This results in a more effective and seamless professional collaborations. Cloud-based solutions also make information technology (IT) management easier by automatically installing software updates, storing backups and eliminating the need for high-specification and expensive computers for data processing and storage. The ability to upload more than one just data-set at a time also provides greater flexibility, while security matters are handled using log-in credentials. In the present work, we discuss a comparative study between two data processing approaches: in one case the data captured by an UAV has been processed using an on-board low-cost single-board computer; in the other case, the captured data has been off-loaded to the cloud for further processing. Processing times between these two approaches are compared, statistical analysis applied, in order to confirm the superiority of the cloud-based processing approach. Challenges inherent with cloud-based processing have also been identified, which will be the subject of future research work.\",\"PeriodicalId\":412628,\"journal\":{\"name\":\"2019 IEEE Women in Engineering (WIE) Forum USA East\",\"volume\":\"660 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Women in Engineering (WIE) Forum USA East\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WIEForum47344.2019.8981670\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Women in Engineering (WIE) Forum USA East","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WIEForum47344.2019.8981670","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Leveraging the Cloud to Achieve Near Real-time Processing for Drone-Generated Data
Low-cost drones are an emerging technological area that open the horizon for intelligent new Internet-of-Things (IoT) and a host of other applications. Cloud-based online processing of unmanned aerial vehicle (UAV) captured data is an attractive and interesting option toward real-time data processing, as it accommodates viewing and analyzing data from a variety of sources, thereby making data accessibility across devices significantly smooth and easier. This results in a more effective and seamless professional collaborations. Cloud-based solutions also make information technology (IT) management easier by automatically installing software updates, storing backups and eliminating the need for high-specification and expensive computers for data processing and storage. The ability to upload more than one just data-set at a time also provides greater flexibility, while security matters are handled using log-in credentials. In the present work, we discuss a comparative study between two data processing approaches: in one case the data captured by an UAV has been processed using an on-board low-cost single-board computer; in the other case, the captured data has been off-loaded to the cloud for further processing. Processing times between these two approaches are compared, statistical analysis applied, in order to confirm the superiority of the cloud-based processing approach. Challenges inherent with cloud-based processing have also been identified, which will be the subject of future research work.