TY  - CONF
ID  - SisLab3185
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3185/
A1  - Dinh, Thi Thai Mai
A1  - Duong, Ngoc Son
A1  - Trinh, Vu Tuan Anh
Y1  - 2018/09/12/
N2  - In this age of IoT (Internet of Things), Indoor
Positioning (IPS) is considered as one of the most popular topicsand has been researched widely all around the world, as the result of various applications it can provide. However, IPS is also a challenging topic that has a number of stringent requirements,such as cost, energy efficiency, availability and accuracy. The
development of Bluetooth Low Energy (BLE) iBeacon has opened great opportunities for researchers to solve those challenges. In this paper, we present our iBeacon based positioning system, which we built as an application running on iOS platform. We also present Fingerprinting ? the main positioning technique used in our system, in which we configure its fingerprints to improve accuracy. With that, a machine learning algorithm called
k-Nearest Neighbor (kNN) is applied to extract the most probable user location. In addition, we also use Kalman Filter in order to enhance the stability of iBeacon?s signal. Our system results in a 60% ? 71.4% accuracy rate and an error of up to 1.6 m, which is acceptable in IPS.
TI  - Bluetooth Low Energy based Indoor Positioning on iOS platform
M2  - Hanoi city, Vietnam
AV  - public
T2  - IEEE 12th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC-2018)
ER  -