BLE RSSI dataset for Indoor localization
Donated on 5/28/2019
This dataset contains RSSIs obtained on smartphones(Sony Xperia XA1). Signals were transmitted from BLE product called iTAG. Location column denotes the position of iTAG in building's entry.
Dataset Characteristics
Sequential, Time-Series
Subject Area
Computer Science
Associated Tasks
Classification
Feature Type
Integer
# Instances
23570
# Features
5
Dataset Information
Additional Information
The dataset was collected with help of students. Twelve students were divided to three groups and each student had iTAG device. They walked inside their limited area with activated iTAG. In long corridor, 18.35m x 3m, we denoted 3 areas which illustrate building's entry: inside, in vestibule and outside. Two smartphones, Sony Xperia XA1, received signals. They located at the start and end of 'in vestibule' area, which has length of 2.35m. Collection of RSSIs lasted for 20 minutes. There are two datasets: filtered_rssi and raw_rssi. We used feedback filter to smooth RSSI. Raw RSSIs are actual RSSIs that smartphone got.
Has Missing Values?
No
Variable Information
name - MAC address of iTAG locationStatus - one of three possible iTAG location: INSIDE, IN_VESTIBULE, OUTSIDE timestamp - timestamp in milliseconds rssiOne - RSSI on first smartphone rssiTwo - RSSI on second smartphone
Dataset Files
| File | Size |
|---|---|
| raw_rssi.csv | 654.8 KB |
| filtered_rssi.csv | 481.7 KB |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset ble_rssi_dataset_for_indoor_localization = fetch_ucirepo(id=586) # data (as pandas dataframes) X = ble_rssi_dataset_for_indoor_localization.data.features y = ble_rssi_dataset_for_indoor_localization.data.targets # metadata print(ble_rssi_dataset_for_indoor_localization.metadata) # variable information print(ble_rssi_dataset_for_indoor_localization.variables)
BLE RSSI dataset for Indoor localization [Dataset]. (2019). UCI Machine Learning Repository. https://doi.org/10.24432/C5B62T.
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.