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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 1, 2020.
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%.
Syifaul Fuada, Trio Adiono, Prasetiyo and Hartian Widhanto Shorful Islam, “Modelling an Indoor Crowd Monitoring System based on RSSI-based Distance” International Journal of Advanced Computer Science and Applications(IJACSA), 11(1), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110181
@article{Fuada2020,
title = {Modelling an Indoor Crowd Monitoring System based on RSSI-based Distance},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110181},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110181},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {1},
author = {Syifaul Fuada and Trio Adiono and Prasetiyo and Hartian Widhanto Shorful Islam}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.