eprintid: 4509
rev_number: 7
eprint_status: archive
userid: 291
dir: disk0/00/00/45/09
datestamp: 2021-06-28 02:07:41
lastmod: 2021-06-28 02:07:41
status_changed: 2021-06-28 02:07:41
type: conference_item
metadata_visibility: show
creators_name: Duong, Ngoc Son
creators_name: Dinh, Thi Thai Mai
creators_id: duongson.vnu@gmail.com
creators_id: dttmai@vnu.edu.vn
title: On the accuracy of iBeacon-based Indoor Positioning System in the iOS platform
ispublished: pub
subjects: Communications
divisions: fac_fet
abstract: Recently, Location-Based Service (LBS) in the indoor field where Global Positioning System (GPS) signal is not available has attracted many researchers. Researchers aim to improve the indoor positioning sys-tem’s performance by proposing algorithms using the supervised learning method, particularly the K-nearest-neighbors-based (KNN-based) fingerprinting method. This research focuses on studying the indoor localization sys-tem’s precision using bluetooth low-energy Received Signal Strength (BLE-RSS) fingerprinting for static scenarios. In the scenario, we use the traditional offline phase to collect the BLE RSSI data and then estimate the user’s position in the online phase based on on-the-fly RSSI measurement  and data from the offline phase. In real-world experiments, the system’s performance has been evaluated in many scenarios to determine the optimal value, such as the value of K in KNN algorithm, and the number of beacons that need to be deployed.
date: 2021-03
official_url: https://ieeexplore.ieee.org/document/9429466
full_text_status: none
pres_type: paper
event_title: 2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)
event_type: conference
refereed: FALSE
citation:   Duong, Ngoc Son and Dinh, Thi Thai Mai  (2021) On the accuracy of iBeacon-based Indoor Positioning System in the iOS platform.  In: 2021 18th International Multi-Conference on Systems, Signals & Devices (SSD).