@inproceedings{SisLab4509,
       booktitle = {2021 18th International Multi-Conference on Systems, Signals \& Devices (SSD)},
           month = {March},
           title = {On the accuracy of iBeacon-based Indoor Positioning System in the iOS platform},
          author = {Ngoc Son Duong and Thi Thai Mai Dinh},
            year = {2021},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4509/},
        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.}
}