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TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density.

Lucie Klus^1,2, Roman Klus¹, Elena Simona Lohan¹

¹Electrical Engineering Unit, Tampere University, 33720 Tampere, Finland.

|April 11, 2024

Summary

This study introduces TUJI1, a new Wi-Fi fingerprinting dataset addressing device differences and data sparsity for indoor positioning. It enables reproducible research in Global Navigation Satellite System-denied environments.

Keywords:

Device heterogeneity Fingerprinting Indoor positioning Localization RSS RSSI Wi-Fi

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Area of Science:

Computer Science
Electrical Engineering
Signal Processing

Background:

Indoor positioning is crucial in Global Navigation Satellite System (GNSS)-denied environments.
Existing wireless fingerprinting datasets face challenges with device heterogeneity and sample density.

Purpose of the Study:

To introduce TUJI1, a novel, anonymized IEEE 802.11 Wireless LAN (Wi-Fi) fingerprinting dataset.
To address the limitations of device heterogeneity and sample density in indoor positioning research.

Main Methods:

Collected Received Signal Strength Indicator (RSSI) measurements from 5 commercial devices in a fine-grained grid.
Anonymized the dataset and matched Wi-Fi fingerprints with precise coordinates.
Split the dataset into training and testing subsets for reproducibility.

Main Results:

TUJI1 provides matched Wi-Fi fingerprints and coordinates from diverse commercial devices.
The dataset is structured for fair and reproducible indoor positioning research.
Addresses the critical issue of device heterogeneity in Wi-Fi fingerprinting.

Conclusions:

TUJI1 dataset facilitates advancements in indoor positioning techniques.
Enables robust and reproducible research by mitigating device variability.
Supports the development of accurate positioning solutions in GNSS-obscured areas.