{"title":"The Development of IoT-Smart Basket: Performance Comparison between Edge Computing and Cloud Computing System","authors":"Nandiwardhana Waranugraha, M. Suryanegara","doi":"10.1109/IC2IE50715.2020.9274596","DOIUrl":null,"url":null,"abstract":"This paper aims to develop the Internet-of Things (IoT) Smart-Basket, working on 2 different systems, i.e. Edge Computing and Cloud Computing. To identify the best system, we compare the performance between “Edge Computing” system and “Cloud Computing” system. The system consists of Raspberry Pi hardware and webcam. Python, TFLite, OpenCV, and Google Cloud Vision API software to detect shopping objects. The object detection results are calculated and sent to end-users through the Telegram application. Discussions are presented concerning the Time Performance and RSSI Value between two systems. The results show “Edge Computing” systems have a more stable system with an average processing time of 1.74 sec on Line-of-Sight (LOS) condition and 1.75 sec on Non-Line-of-Sight (NLOS) condition compared to “Cloud Computing” systems with an average processing time of 10.46 sec on LOS condition and 5.36 sec on NLOS condition.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC2IE50715.2020.9274596","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
This paper aims to develop the Internet-of Things (IoT) Smart-Basket, working on 2 different systems, i.e. Edge Computing and Cloud Computing. To identify the best system, we compare the performance between “Edge Computing” system and “Cloud Computing” system. The system consists of Raspberry Pi hardware and webcam. Python, TFLite, OpenCV, and Google Cloud Vision API software to detect shopping objects. The object detection results are calculated and sent to end-users through the Telegram application. Discussions are presented concerning the Time Performance and RSSI Value between two systems. The results show “Edge Computing” systems have a more stable system with an average processing time of 1.74 sec on Line-of-Sight (LOS) condition and 1.75 sec on Non-Line-of-Sight (NLOS) condition compared to “Cloud Computing” systems with an average processing time of 10.46 sec on LOS condition and 5.36 sec on NLOS condition.
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