DC Field | Value | Language |
---|---|---|
dc.contributor.author | Fuada, Syifaul | ko |
dc.contributor.author | Adiono, Trio | ko |
dc.contributor.author | Prasetiyo | ko |
dc.contributor.author | Islam, Hartian Widhanto Shorful | ko |
dc.date.accessioned | 2021-03-26T03:54:11Z | - |
dc.date.available | 2021-03-26T03:54:11Z | - |
dc.date.created | 2020-03-30 | - |
dc.date.created | 2020-03-30 | - |
dc.date.created | 2020-03-30 | - |
dc.date.issued | 2020-01 | - |
dc.identifier.citation | INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, v.11, no.1, pp.660 - 667 | - |
dc.identifier.issn | 2158-107X | - |
dc.identifier.uri | http://hdl.handle.net/10203/282119 | - |
dc.description.abstract | This paper reports a real-time localization algorithm system that has a main function to determine the location of devices accurately. The model can locate the smartphone position passively (which do not need a set on a smartphone) as long as the Wi-Fi is turned on. The algorithm uses Intersection Density, and the Nonlinear Least Square Algorithm (NLS) method that utilizes the Lavenberg-Marquart method. To minimize the localization error, Kalman Filter (KF) is used. The algorithm is computed under Matlab approach. The most obtained model will be implemented in this Wi-Fi tracker system using RSSI-based distance for indoor crowd monitoring. According to the experiment result, KF can improve Hit ratio of 81.15 %. Hit ratio is predicting results of a location that is less than 5 m from the actual area (location). It can be obtained from several RSSI scans, the calculation is as follows: the number of non-error results divided by the number of RSSI scans and multiplied by 100%. | - |
dc.language | English | - |
dc.publisher | SCIENCE & INFORMATION SAI ORGANIZATION LTD | - |
dc.title | Modelling an Indoor Crowd Monitoring System based on RSSI-based Distance | - |
dc.type | Article | - |
dc.identifier.scopusid | 2-s2.0-85087094998 | - |
dc.type.rims | ART | - |
dc.citation.volume | 11 | - |
dc.citation.issue | 1 | - |
dc.citation.beginningpage | 660 | - |
dc.citation.endingpage | 667 | - |
dc.citation.publicationname | INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS | - |
dc.identifier.doi | 10.14569/IJACSA.2020.0110181 | - |
dc.contributor.nonIdAuthor | Fuada, Syifaul | - |
dc.contributor.nonIdAuthor | Adiono, Trio | - |
dc.contributor.nonIdAuthor | Islam, Hartian Widhanto Shorful | - |
dc.description.isOpenAccess | Y | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Wi-Fi tracker system | - |
dc.subject.keywordAuthor | RSSI-based distance | - |
dc.subject.keywordAuthor | intersection density method | - |
dc.subject.keywordAuthor | Nonlinear Least Square (NLS) method | - |
dc.subject.keywordAuthor | Kalman Filter (KF) | - |
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