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.ĭespite intense research on wireless sensor networks (WSN) in the last two decades, programmers still do not have a cohesive and expressive application programming interface (API) to model their applications and to connect them to the internet of things (IoT). The beacon simulator was less accurate and had a maximum error of 60.5% and a minimum of 4%.
From each beacon were calculated four different distances, Nearby yielding a maximum error of 26% and a minimum of 4%. The received signal strength interface (RSSI) was measured and used to calculate the Euclidean distance. This method offers a better trade-off between cost, power, and accuracy.Nearby API and a regular beacon simulator application were used as beacons. 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. 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. 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.
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