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Geo-Magnetic field and WLAN dataset for indoor localisation from wristband and smartphone Data Set
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Abstract: A multisource and multivariate dataset for indoor localisation methods based on WLAN and Geo-Magnetic field fingerprinting

Data Set Characteristics:  

Multivariate, Sequential, Time-Series

Number of Instances:

153540

Area:

Computer

Attribute Characteristics:

Integer, Real

Number of Attributes:

25

Date Donated

2017-01-10

Associated Tasks:

Classification, Regression, Clustering

Missing Values?

N/A

Number of Web Hits:

6938


Source:

Paolo Barsocchi, Antonino Crivello, Davide La Rosa, Filippo Palumbo

Italian National Council of Research, ISTI-CNR, Pisa, Italy
{name.surname}@isti.cnr.it


Data Set Information:

Indoor localisation is a key topic for the Ambient Intelligence (AmI) research community.

In this scenarios, recent advancements in wearable technologies, particularly smartwatches with built-in sensors, and personal devices, such as smartphones, are being seen as the breakthrough for making concrete the envisioned Smart Environment (SE) paradigm.

In particular, scenarios devoted to indoor localization represent a key challenge to be addressed. Many works try to solve the indoor localization issue, but the lack of a common dataset or frameworks to compare and evaluate solutions represent a big barrier to be overcome in the field. The unavailability and uncertainty of public datasets hinders the possibility to compare different indoor localization algorithms. This constitutes the main motivation of the proposed dataset described herein.

We collected Wi-Fi and geo-magnetic field fingerprints, together with inertial sensor data during two campaigns performed in the same environment. Retrieving syncronized data from a smartwatch and a smartphone worn by users at the purpose of create and present a public available dataset is the goal of this work.


Attribute Information:

Pointsmapping.ods:
A three column spreadsheet (ID,X,Y) which points mapping in local coordinates.
Each ID represents an unique place on the map. The X-Y coordinates represents the local coordinates.

For each measure:

measure1(2)_timestamp_id.csv:

Timestamp (Unixtime) of arrival on placeID, timestamp (Unixtime) of departure by placeID, Place ID identifier (0-324)

measure1(2)_smartphone_sens.csv:

According to measure1(2)_timestamp_id.csv, this csv contains the data sensors retrieved by the smartphone.
Timestamp, AccelerationX, AccelerationY, AccelerationZ, MagneticFieldX, MagneticFieldY, MagneticFieldZ, Z-AxisAgle(Azimuth), X-AxisAngle(Pitch), Y-AxisAngle(Roll), GyroX, GyroY, GyroZ

measure1(2)_smartwatch_sens.csv:

According to measure1(2)_timestamp_id.csv, this csv contains the data sensors retrieved by the smartwatch.
Timestamp, AccelerationX, AccelerationY, AccelerationZ, MagneticFieldX, MagneticFieldY, MagneticFieldZ, Z-AxisAgle(Azimuth), X-AxisAngle(Pitch), Y-AxisAngle(Roll), GyroX, GyroY, GyroZ

measure1(2)_smartphone_wifi.csv:

Each rows contains PlaceId (ascending order) and 127 column, with RSSI level for each different
WAPs retrieved during the campaign. Not all the WAPs are detected in each scan.
For these WAPs, the articial RSSI value is -100 (dbm).


Relevant Papers:

Barsocchi, P., Crivello, A., La Rosa, D., & Palumbo, F. (2016, October). A multisource and multivariate dataset for indoor localization methods based on WLAN and geo-magnetic field fingerprinting. In Indoor Positioning and Indoor Navigation (IPIN), 2016 International Conference on (pp. 1-8). IEEE.



Citation Request:

Barsocchi, P., Crivello, A., La Rosa, D., & Palumbo, F. (2016, October). A multisource and multivariate dataset for indoor localization methods based on WLAN and geo-magnetic field fingerprinting. In Indoor Positioning and Indoor Navigation (IPIN), 2016 International Conference on (pp. 1-8). IEEE.


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