Data Set Information:

室内定位是环境智能（AmI）研究界的一个关键课题。

在这种情况下，可穿戴技术的最新进展，特别是内置传感器的智能手表和智能手机等个人设备，被视为实现设想的智能环境（SE）模式的突破。

特别是，室内本地化场景是需要解决的关键挑战。许多工作试图解决室内本地化问题，但缺乏一个通用的数据集或框架来比较和评估解决方案，这是该领域需要克服的一大障碍。公共数据集的不可用性和不确定性阻碍了比较不同室内定位算法的可能性。这构成了本文所述拟议数据集的主要动机。

我们收集了在同一环境下进行的两次活动期间的Wi-Fi和地磁指纹以及惯性传感器数据。这项工作的目标是从用户佩戴的smartwatch和智能手机中检索同步数据，以创建和呈现公共可用数据集。

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.

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

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