Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap

Brahim El Boudani*, Tasos Dagiuklas, Loizos Kanaris, Muddesar Iqbal, Christos Chrysoulas

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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.
Original languageEnglish
Article number4150
JournalElectronics
Volume12
Issue number19
Early online date5 Oct 2023
DOIs
Publication statusPublished - 5 Oct 2023

Keywords

  • indoor localisation
  • 5G IoT
  • deep learning
  • machine learning
  • information fusion
  • tracking
  • Internet of Things

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