Modelling an Indoor Crowd Monitoring System based on RSSI-based Distance

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%.

@article{Fuada2020,
  title     = {Modelling an Indoor Crowd Monitoring System based on RSSI-based Distance},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {1},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Syifaul Fuada and Trio Adiono and Prasetiyo and Hartian Widhanto Shorful Islam},
  doi       = {10.14569/IJACSA.2020.0110181},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110181}
}