The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVI-4/W6-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 117–124, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-117-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 117–124, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-117-2021

  18 Nov 2021

18 Nov 2021

INVESTIGATION OF BLUETOOTH LOW ENERGY (BLE) AND WIRELESS LOCAL AREA NETWORK (WLAN)-DERIVED DISTANCE MEASUREMENTS USING MOBILE PHONES FOR INDOOR POSITIONING

K. M. K. Daray, P. A. G. Todoc, and C. J. S. Sarmiento K. M. K. Daray et al.
  • Department of Geodetic Engineering, University of the Philippines Diliman, Osmena Avenue, Diliman, Quezon City, Philippines

Keywords: Bluetooth Low Energy, Wireless Local Area Network, Distance, Mobile Phones, Indoor Positioning, Google Nearby, Beacon

Abstract. The indoor positioning problem is not the unavailability of indoor positioning technology, but the difficulty of arriving at an acceptable compromise of technical constraints like cost, performance, ease of use, and availability of technologies. In a developing country such as the Philippines, these constraints have more weight and can restrict the advancement of indoor positioning.

This study investigates the use of Bluetooth Low Energy (BLE) and Wireless Local Area Network (WLAN) in Euclidean distance computation, which implies prospect use for indoor positioning through trilateration. It is a proof-of-concept study that BLE and WLAN, using readily-available services such as Nearby Application Programming Interface (API) and beacon simulators, can be used for indoor positioning. This method offers a better trade-off between cost, power, and accuracy.

Nearby API and a regular beacon simulator application were used as beacons. The received signal strength interface (RSSI) was measured and used to calculate the Euclidean distance. From each beacon were calculated four different distances, Nearby yielding a maximum error of 26% and a minimum of 4%. The beacon simulator was less accurate and had a maximum error of 60.5% and a minimum of 4%. This shows that it is possible to calculate Euclidean distance using WLAN and BLE, and that Nearby API, which uses both, was more accurate than the beacon simulator, which used only BLE.