Open AccessOptimization BLE Power Beacon for Indoor Locations Static Smart Device with Gaussian Filter
Author(s) -
Fanda Lyta Suzanayanti,
Mudrik Alaydrus
Publication year - 2021
Publication title -
incomtech : jurnal telekomunikasi dan komputer/incomtech : jurnal telekomunikasi dan komputer
Language(s) - English
Resource type - Journals
eISSN - 2579-6089
pISSN - 2085-4811
DOI - 10.22441/incomtech.v11i1.9811
Subject(s) - beacon , gaussian filter , gaussian , computer science , power (physics) , filter (signal processing) , position (finance) , transmitter power output , line of sight , real time computing , electronic engineering , telecommunications , engineering , transmitter , computer vision , physics , channel (broadcasting) , finance , quantum mechanics , economics , image (mathematics) , aerospace engineering
BLE beacon in an indoor location, battery efficiency usage must be right. Power beacons as one of the keys that need to be optimized. The decrease in power beacons will decrease the estimated distance from an indoor location based on the RSSI value. Therefore, an additional method is needed to recover the estimated distance's accuracy value due to the reduced power. In this paper, the method for recovery accuracy is by using a gaussian filter. Measurements were made at the same position on 3 BLE signals from multipower transmitters, which differed in their transmit power (TX power -1 dBm, -9 dBm and -20 dBm). The first six points are selected with the position of the line of sight as environment 1, and the second six points are chosen with the position of non-line of sight as environment 2 (obstructed). The first point of each environment is used as a reference. In environment 1, transmit power reduces the 24 dB effect to a decrease in accuracy distance estimation. The Gaussian effectiveness filter for improvement accuracy at all measurement points or 100%. In environment 2, reduce power transmit 12 dB is not followed by a decrease in accuracy distance estimation. The effectiveness of the Gaussian filter for improving accuracy is 60% of the number of measurement points. Finally, the Gaussian filter in the power optimization can provide recovery accuracy distance estimation is 80% from measurement sample for environment 1 and environment 2.