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Related Experiment Video

Updated: Jun 25, 2026

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

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Indoor environment dataset based on RSSI collected with bluetooth devices.

Yuri Assayag¹, Horacio Oliveira¹, Max Lima¹

¹Institute of Computing, Federal University of Amazonas, Amazonas, Brazil.

|July 29, 2024

Summary

This summary is machine-generated.

This study collected Bluetooth Low Energy (BLE) signal data from 11 mobile devices across 150 test points for indoor positioning research. The dataset aids in developing accurate indoor localization models.

Keywords:

Bluetooth Indoor localization Internet of things Location based services Received signal strength indicator

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Area of Science:

Computer Science
Electrical Engineering
Robotics

Background:

Accurate indoor positioning is crucial for various applications, including navigation, asset tracking, and augmented reality.
Existing indoor positioning systems often face challenges with accuracy, scalability, and cost-effectiveness.
Bluetooth Low Energy (BLE) offers a promising technology for low-power, cost-effective indoor localization.

Purpose of the Study:

To present a comprehensive dataset for the development and evaluation of indoor positioning systems using BLE.
To provide researchers with real-world experimental data to advance indoor localization algorithms.
To facilitate the creation of more robust and precise indoor positioning solutions.

Main Methods:

Conducted a data collection experiment in a real-world indoor environment.
Utilized 11 mobile devices and 15 fixed BLE anchor nodes.
Recorded Received Signal Strength Indicator (RSSI) values from mobile devices to anchor nodes at 150 distinct test points.
Collected associated data including device identification, test point coordinates, and room labels.

Main Results:

A structured dataset containing RSSI measurements, device information, and spatial-temporal data was generated.
The dataset captures real-world signal variations crucial for algorithm training and validation.
Data is organized into easily accessible CSV files for broad research utility.

Conclusions:

The released dataset provides a valuable resource for the indoor positioning research community.
This data can significantly contribute to the advancement of machine learning-based and other indoor localization techniques.
The availability of this dataset will accelerate the development of practical and high-performance indoor positioning systems.