DOI: 10.5281/zenodo.12760027
title: WiFi RTT RSS dataset for indoor positioning

description: This is the first batch of WiFi RSS RTT datasets with LOS conditions we published. Please see https://doi.org/10.5281/zenodo.11558792 for the second batch.

Please do use version 2 for better quality.

We provide publicly available datasets of three different indoor scenarios: building floor, office and apartment. The datasets contain both WiFi RSS and RTT signal measures with groud truth coordinates label and LOS condition label.

1.Building Floor

This is a detailed WiFi RTT and RSS dataset of a whole floor of a university building, of moare than 92 x 15 square metres. We divided the area of interest was divided into discrete grids and labelled them with correct ground truth coordinates and the LoS APs from the grid. The dataset contains WiFi RTT and RSS signal measures recorded in 642 reference points for 3 days and is well separated so that training points and testing points will not overlap.

2. Office

Office scenario is of more than 4.5 x 5.5 square metres. 3 APs are set to cover the whole space. At least two LOS AP could be seen at any reference point (RP). 

3.Apartment

Apartment scenario is of more than 7.7 x 9.4 square metres.Four APs were leveraged to generate WiFi signal measures for this testbed. Note that AP 1 in the apartment dataset was positioned so that it could had an NLOS path to most of the testbed. 

 

Collection methodology

The APs utilised were Google WiFi Router AC-1304, the smartphone used to collect the data was Google Pixel 3 with Android 9.

The ground truth coordinates were collected using fixed tile size on the floor and manual post-it note markers. 

Only RTT-enabled APs were included in the dataset.

The features of the datasets

The features of the building floor dataset are as follows:



Testbed area:  92 × 15 m2

Grid size: 0.6 × 0.6 m2

Number of AP: 13

Number of reference points: 642

Samples per reference point: 120

Number of all data samples: 77040

Number of training samples: 57960

Number of testing samples: 19080

Signal measure: WiFi RTT, WiFi RSS

Collection time interval: 3 days


The features of the office dataset are as follows:



Testbed area:  4.5 × 5.5 m2

Grid size: 0.455 × 0.455 m2

Number of AP: 3

Reference points: 37

Samples per reference point: 120

Data samples: 4,440

Training samples: 3,240

Testing samples: 1,200

Signal measure: WiFi RTT, WiFi RSS

Other information: LOS condition of every AP

Collection time: 1 day

Notes: A LOS scenario


The features of the apartment dataset are as follows:



Testbed area:  7.7 × 9.4 m2

Grid size: 0.48 × 0.48 m2

Number of AP: 4

Reference points: 110

Samples per reference point: 120

Data samples: 13,200

Training samples: 9,720

Testing samples: 3,480

Signal measure: WiFi RTT, WiFi RSS

Other information: LOS condition of every AP

Collection time: 1 day

Notes: Contains an AP with NLOS paths for most of the RPs


Dataset explanation

The columns of the dataset are as follows:

Column 'X': the X coordinates of the sample.

Column 'Y': the Y coordinates of the sample.

Column 'AP1 RTT(mm)', 'AP2 RTT(mm)', ..., 'AP13 RTT(mm)': the RTT measure from corresponding AP at a reference point.

Column 'AP1 RSS(dBm)', 'AP2 RSS(dBm)', ..., 'AP13 RSS(dBm)': the RSS measure from corresponding AP at a reference point.

Column 'LOS APs': indicating which AP has a LOS to this reference point.

Please note:



The RSS value -200 dBm indicates that the AP is too far away from the current reference point and no signals could be heard from it.

The RTT value 100,000 mm indicates that no signal is received from the specific AP.


Citation request

When using this dataset, please cite the following two items:Feng, X., Nguyen, K. A., & Luo, Z. (2024). WiFi RTT RSS dataset for indoor positioning [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11558192@article{feng2023wifi, title={WiFi round-trip time (RTT) fingerprinting: an analysis of the properties and the performance in non-line-of-sight environments}, author={Feng, Xu and Nguyen, Khuong an and Luo, Zhiyuan}, journal={Journal of Location Based Services}, volume={17}, number={4}, pages={307--339}, year={2023}, publisher={Taylor \& Francis} }