wireless-localization-frs-uofu 0.1.2

A library to fetch my dataset in JSON/CSV format.

Wireless Device Localization Dataset

Dataset Description

This dataset contains a large-scale collection of measurements for RSS-based localization. The data was collected using the POWDER Testbed at the University of Utah and includes received signal strength (RSS) measurements with either 0, 1, or 2 active transmitters.

How to use its

• Install the dataset using pip install wireless-localization-frs-uofu
• Import the dataset loader from wireless_localization_frs_uofu.datasets import load_dataset
• To import the full Dataset: load_dataset("frs-dataset")
• To import only the single transmitter dataset in csv: load_dataset("single_tx_csv") or in json: load_dataset("single_tx_json")
• • To import only the two transmitter dataset in csv: load_dataset("two_tx_csv") or in json: load_dataset("two_tx_json")

Overview

• Total Samples: 5,214 unique samples
• Transmitter Locations: 5,514 unique locations
• Receiver Count per Sample: 10–25 receivers

Sample Distribution

Sample Type	Count	Receiver Count
No Transmitters	46	10–25
1 Transmitter	4,822	10–25
2 Transmitters	346	11–12

The transmitters are handheld walkie-talkies (Baofeng BF-F8HP) operating in the FRS/GMRS band at 462.7 MHz, with a rated transmission power of 1 W. The RSS values were derived from raw IQ samples processed through a 6 kHz bandpass filter to eliminate neighboring transmissions, and the RSS was calculated as follows:

$$RSS = \frac{10}{N} \log_{10}\left(\sum_{i=1}^N x_i^2 \right)$$

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Measurement Parameters

Parameter	Value
Frequency	462.7 MHz
Radio Gain	35 dB
Receiver Sample Rate	2 MHz
Sample Length	N = 10,000
Band-pass Filter	6 kHz
Transmitters	0–2
Transmission Power	1 W

Receivers:

The receivers include Ettus USRP X310 and B210 radios, equipped with a mix of wide- and narrow-band antennas. RSS values are uncalibrated and only relative to the device, as no calibration data was available. Each receiver took measurements with a receiver gain of 35 dB. However, devices have different maxmimum gain settings, and no calibration data was available, so all RSS values in the dataset are uncalibrated, and are only relative to the device.

The majority of the receivers are stationary endpoints fixed on the side of buildings, on rooftop towers, or on free-standing poles. A small set of receivers are located on shuttles which travel specific routes throughout campus.

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Dataset Structure

Data Format

The dataset is provided in both .json and .csv format, both as a single file and as split files.

.json Structure

Below is a sample JSON sample of two transmitter case:

{
  "2022-04-25 14:11:02": {
    "rx_data": [
      [
        -75.14881216502957,
        40.76695251464844,
        -111.85211944580078,
        "bus-4603"
      ],
      [
        -72.76890586248369,
        40.772705078125,
        -111.83783721923828,
        "bus-6183"
      ],
      [
        -66.9426657074761,
        40.76134,
        -111.84629,
        "cbrssdr1-bes-comp"
      ],
      [
        -82.52322009127514,
        40.7644,
        -111.83699,
        "cbrssdr1-honors-comp"
      ],
      [
        -68.77001181251623,
        40.77105,
        -111.83712,
        "cbrssdr1-hospital-comp"
      ],
      [
        -64.15222248890146,
        40.76895,
        -111.84167,
        "cbrssdr1-ustar-comp"
      ],
      [
        -68.39949252257873,
        40.7677,
        -111.83816,
        "ebc-nuc1-b210"
      ],
      [
        -78.83858666321109,
        40.76148,
        -111.84201,
        "garage-nuc1-b210"
      ],
      [
        -84.66956126342163,
        40.76627,
        -111.83632,
        "guesthouse-nuc2-b210"
      ],
      [
        -95.0148341336122,
        40.7616,
        -111.85185,
        "law73-nuc1-b210"
      ],
      [
        -91.05168678465658,
        40.75786,
        -111.83634,
        "madsen-nuc1-b210"
      ],
      [
        -82.40519021775879,
        40.76278,
        -111.83061,
        "sagepoint-nuc1-b210"
      ]
    ],
    "tx_coords": [
      [
        40.76778075,
        -111.84686963
      ],
      [
        40.76935595,
        -111.84657217
      ]
    ],
    "metadata": [
      {
        "power": 1,
        "transport": "walking",
        "radio": "TXA"
      },
      {
        "power": 1,
        "transport": "driving",
        "radio": "TXB"
      }
    ]
  }
}

• rx_data: A list of RSS data from each receiver, including RSS value, latitude, longitude, and device name.
• tx_coords: Coordinates (latitude and longitude) for each transmitter.
• metadata: Metadata for each transmitter, aligned with tx_coords.

How to Load Data in Python:

import json

data_file = 'powder_462.7_rss_data.json'
with open(data_file) as f:
    data = json.load(f)

.csv Structure

The Dataset also contains single_tx.csv and two_tx.csv files. This is the csv format representation of original json dataset. The columns of these two datasets are timestamp, rss values of each receiver nodes and the coordinates of the transmitters. A separate file location_coordinates.json contains the coordinates of all the stationary receiver nodes. For the moveable receivers (name starts with 'bus'), two columns in the .csv file includes the latitude and longtitude of a moveable node. Therefore, in the .csv files, there is three columns for each of the moveable receiver node with rss value, x coordinate and y coordinate of the node.

Digital Surface Model

The dataset includes a digital surface model (DSM) from a State of Utah 2013-2014 LiDAR survey. This map includes the University of Utah campus and surrounding area. The DSM includes buildings and trees, unlike some digital elevation models.

To read the data in python:

import rasterio as rio
import numpy as np
import utm

dsm_object = rio.open('dsm.tif')
dsm_map = dsm_object.read(1)     # a np.array containing elevation values
dsm_resolution = dsm_object.res     # a tuple containing x,y resolution (0.5 meters) 
dsm_transform = dsm_object.transform     # an Affine transform for conversion to UTM-12 coordinates
utm_transform = np.array(dsm_transform).reshape((3,3))[:2]
utm_top_left = utm_transform @ np.array([0,0,1])
utm_bottom_right = utm_transform @ np.array([dsm_object.shape[0], dsm_object.shape[1], 1])
latlon_top_left = utm.to_latlon(utm_top_left[0], utm_top_left[1], 12, 'T')
latlon_bottom_right = utm.to_latlon(utm_bottom_right[0], utm_bottom_right[1], 12, 'T')

Dataset Acknowledgement: This DSM file is acquired by the State of Utah and its partners, and is in the public domain and can be freely distributed with proper credit to the State of Utah and its partners. The State of Utah and its partners makes no warranty, expressed or implied, regarding its suitability for a particular use and shall not be liable under any circumstances for any direct, indirect, special, incidental, or consequential damages with respect to users of this product.

DSM DOI: https://doi.org/10.5069/G9TH8JNQ