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On the accuracy of iBeacon-based Indoor Positioning System in the iOS platform

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).

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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.

Item Type: Conference or Workshop Item (Paper)
Subjects: Electronics and Communications > Communications
Divisions: Faculty of Electronics and Telecommunications (FET)
Depositing User: Dr. Thai-Mai Dinh Thi
Date Deposited: 28 Jun 2021 02:07
Last Modified: 28 Jun 2021 02:07

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