Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap
Journal article
El Boudani, B., Dagiuklas, A., Kanaris, L., Iqbal, M. and Chrysoulas, C. (2023). Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap. Electronics. 12 (19), p. 4150. https://doi.org/10.3390/electronics12194150
Authors | El Boudani, B., Dagiuklas, A., Kanaris, L., Iqbal, M. and Chrysoulas, C. |
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Abstract | Indoor positioning is a core enabler for various 5G identity and context-aware applications requiring precise and real-time simultaneous localisation and mapping (SLAM). In this work, we propose a K-nearest neighbours and deep neural network (K-DNN) algorithm to improve 3D indoor positioning. Our implementation uses a novel data-augmentation concept for the received signal strength (RSS)-based fingerprint technique to produce a 3D fused hybrid. In the offline phase, a machine learning (ML) approach is used to train a model on a radiomap dataset that is collected during the offline phase. The proposed algorithm is implemented on the constructed hybrid multi-layered radiomap to improve the 3D localisation accuracy. In our implementation, the proposed approach is based on the fusion of the prominent 5G IoT signals of Bluetooth Low Energy (BLE) and the ubiquitous WLAN. As a result, we achieved a 91% classification accuracy in 1D and a submeter accuracy in 2D. |
Year | 2023 |
Journal | Electronics |
Journal citation | 12 (19), p. 4150 |
Publisher | MDPI |
ISSN | 2079-9292 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/electronics12194150 |
Web address (URL) | https://doi.org/10.3390/electronics12194150 |
Publication dates | |
Online | 05 Oct 2023 |
Publication process dates | |
Accepted | 29 Sep 2023 |
Deposited | 12 Oct 2023 |
Publisher's version | License File Access Level Open |
https://openresearch.lsbu.ac.uk/item/95348
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