UJIIndoorLoc数据集 可用于分类，如实际建筑物和楼层识别

Donors/Contact Joaquín Torres-Sospedra jtorres +@+ uji.es Raul Montoliu montoliu +@+ uji.es Adolfo Martínez-Usó admarus +@+ upv.es Joaquín Huerta huerta +@+ uji.es UJI - Institute of New Imaging Technologies, Universitat Jaume I, Avda. Vicente Sos Baynat S/N, 12071, Castellón, Spain. UPV - Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València, Valencia, Spain. Creators Joaquín Torres-Sospedra, Raul Montoliu, Adolfo Martínez-Usó, Tomar J. Arnau, Joan P. Avariento, Mauri Benedito-Bordonau, Joaquín Huerta, Yasmina Andreu, óscar Belmonte, Vicent Castelló, Irene Garcia-Martí, Diego Gargallo, Carlos Gonzalez, Nadal Francisco, Josep López, Ruben Martínez, Roberto Mediero, Javier Ortells, Nacho Piqueras, Ianisse Quizán, David Rambla, Luis E. Rodríguez, Eva Salvador Balaguer, Ana Sanchís, Carlos Serra, and Sergi Trilles. Data Set Information: Many real world applications need to know the localization of a user in the world to provide their services. Therefore, automatic user localization has been a hot research topic in the last years. Automatic user localization consists of estimating the position of the user (latitude, longitude and altitude) by using an electronic device, usually a mobile phone. Outdoor localization problem can be solved very accurately thanks to the inclusion of GPS sensors into the mobile devices. However, indoor localization is still an open problem mainly due to the loss of GPS signal in indoor environments. Although, there are some indoor positioning technologies and methodologies, this database is focused on WLAN fingerprint-based ones (also know as WiFi Fingerprinting). Although there are many papers in the literature trying to solve the indoor localization problem using a WLAN fingerprint-based method, there still exists one important drawback in this field which is the lack of a common database for comparison purposes. So, UJIIndoorLoc database is presented to overcome this gap. We expect that the proposed database will become the reference database to compare different indoor localization methodologies based on WiFi fingerprinting. The UJIIndoorLoc database covers three buildings of Universitat Jaume I with 4 or more floors and almost 110.000m2. It can be used for classification, e.g. actual building and floor identification, or regression, e.g. actual longitude and latitude estimation. It was created in 2013 by means of more than 20 different users and 25 Android devices. The database consists of 19937 training/reference records (trainingData.csv file) and 1111 validation/test records (validationData.csv file). The 529 attributes contain the WiFi fingerprint, the coordinates where it was taken, and other useful information. Each WiFi fingerprint can be characterized by the detected Wireless Access Points (WAPs) and the corresponding Received Signal Strength Intensity (RSSI). The intensity values are represented as negative integer values ranging -104dBm (extremely poor signal) to 0dbM. The positive value 100 is used to denote when a WAP was not detected. During the database creation, 520 different WAPs were detected. Thus, the WiFi fingerprint is composed by 520 intensity values. Then the coordinates (latitude, longitude, floor) and Building ID are provided as the attributes to be predicted. Additional information has been provided. The particular space (offices, labs, etc.) and the relative position (inside/outside the space) where the capture was taken have been recorded. Outside means that the capture was taken in front of the door of the space. Information about who (user), how (android device & version) and when (timestamp) WiFi capture was taken is also recorded. Attribute Information: Attribute 001 (WAP001): Intensity value for WAP001. Negative integer values from -104 to 0 and +100. Positive value 100 used if WAP001 was not detected. .... Attribute 520 (WAP520): Intensity value for WAP520. Negative integer values from -104 to 0 and +100. Positive Vvalue 100 used if WAP520 was not detected. Attribute 521 (Longitude): Longitude. Negative real values from -7695.9387549299299000 to -7299.786516730871000 Attribute 522 (Latitude): Latitude. Positive real values from 4864745.7450159714 to 4865017.3646842018. Attribute 523 (Floor): Altitude in floors inside the building. Integer values from 0 to 4. Attribute 524 (BuildingID): ID to identify the building. Measures were taken in three different buildings. Categorical integer values from 0 to 2. Attribute 525 (SpaceID): Internal ID number to identify the Space (office, corridor, classroom) where the capture was taken. Categorical integer values. Attribute 526 (RelativePosition): Relative position with respect to the Space (1 - Inside, 2 - Outside in Front of the door). Categorical integer values. Attribute 527 (UserID): User identifier (see below). Categorical integer values. Attribute 528 (PhoneID): Android device identifier (see below). Categorical integer values. Attribute 529 (Timestamp): UNIX Time when the capture was taken. Integer value. --------------------------------------------- UserID Anonymized user Height (cm) --------------------------------------------- 0 USER0000 (Validation User) N/A 1 USER0001 170 2 USER0002 176 3 USER0003 172 4 USER0004 174 5 USER0005 184 6 USER0006 180 7 USER0007 160 8 USER0008 176 9 USER0009 177 10 USER0010 186 11 USER0011 176 12 USER0012 158 13 USER0013 174 14 USER0014 173 15 USER0015 174 16 USER0016 171 17 USER0017 166 18 USER0018 162 ---------------------------------------------- ---------------------------------------------- PhoneID Android Device Android Ver. UserID ---------------------------------------------- 0 Celkon A27 4.0.4(6577) 0 1 GT-I8160 2.3.6 8 2 GT-I8160 4.1.2 0 3 GT-I9100 4.0.4 5 4 GT-I9300 4.1.2 0 5 GT-I9505 4.2.2 0 6 GT-S5360 2.3.6 7 7 GT-S6500 2.3.6 14 8 Galaxy Nexus 4.2.2 10 9 Galaxy Nexus 4.3 0 10 HTC Desire HD 2.3.5 18 11 HTC One 4.1.2 15 12 HTC One 4.2.2 0 13 HTC Wildfire S 2.3.5 0,11 14 LT22i 4.0.4 0,1,9,16 15 LT22i 4.1.2 0 16 LT26i 4.0.4 3 17 M1005D 4.0.4 13 18 MT11i 2.3.4 4 19 Nexus 4 4.2.2 6 20 Nexus 4 4.3 0 21 Nexus S 4.1.2 0 22 Orange Monte Carlo 2.3.5 17 23 Transformer TF101 4.0.3 2 24 bq Curie 4.1.1 12 ---------------------------------------------- Relevant Papers: Joaquín Torres-Sospedra, Raúl Montoliu, Adolfo Martínez-Usó, Tomar J. Arnau, Joan P. Avariento, Mauri Benedito-Bordonau, Joaquín Huerta UJIIndoorLoc: A New Multi-building and Multi-floor Database for WLAN Fingerprint-based Indoor Localization Problems In Proceedings of the Fifth International Conference on Indoor Positioning and Indoor Navigation, 2014. Available at: <a href="http://www.ipi

联系人：Joaquín Torres-Sospedra，邮箱[e-mail]；Raul Montoliu，邮箱[e-mail]；Adolfo Martínez-Usó，邮箱[e-mail]；Joaquín Huerta，邮箱[e-mail]。

机构信息：
UJI - 哈梅一世大学（Universitat Jaume I）新成像技术研究所，地址：Avda. Vicente Sos Baynat S/N, 12071, 卡斯特利翁，西班牙。
UPV - 巴伦西亚理工大学（Universitat Politècnica de València）计算机系统与计算机科学系，西班牙巴伦西亚。

创建者：Joaquín Torres-Sospedra、Raul Montoliu、Adolfo Martínez-Usó、Tomar J. Arnau、Joan P. Avariento、Mauri Benedito-Bordonau、Joaquín Huerta、Yasmina Andreu、Óscar Belmonte、Vicent Castelló、Irene Garcia-Martí、Diego Gargallo、Carlos Gonzalez、Nadal Francisco、Josep López、Ruben Martínez、Roberto Mediero、Javier Ortells、Nacho Piqueras、Ianisse Quizán、David Rambla、Luis E. Rodríguez、Eva Salvador Balaguer、Ana Sanchís、Carlos Serra 以及 Sergi Trilles。

数据集说明：诸多现实应用需要知晓用户的全球位置以提供服务，因此自动用户定位在近年成为热门研究课题。自动用户定位指通过移动电话等电子设备，估算用户的位置（纬度、经度与海拔）。得益于移动设备内置GPS传感器，室外定位可实现高精度求解。然而，室内定位仍为尚未完全解决的难题，主要原因在于室内环境中GPS信号会出现衰减丢失。
尽管目前已有多种室内定位技术与方法，但本数据集聚焦于基于WLAN指纹（又称WiFi指纹）的定位方案。尽管已有诸多文献尝试通过WLAN指纹方法解决室内定位问题，但该领域仍存在一个关键缺陷：缺乏可供统一对比的公共数据集。为此，我们构建了UJIIndoorLoc数据集以填补这一空白，期望该数据集能够成为基于WiFi指纹的各类室内定位方法的标准对比基准。
UJIIndoorLoc数据集覆盖哈梅一世大学的3栋建筑，每栋建筑拥有4层及以上楼层，总占地面积近11万平方米。该数据集可用于分类任务（如识别建筑与楼层）或回归任务（如估算经度与纬度）。该数据集于2013年构建，共使用20余名志愿者与25台安卓设备完成数据采集。
数据集包含19937条训练/参考记录（存储于trainingData.csv文件）与1111条验证/测试记录（存储于validationData.csv文件）。数据集共包含529个属性，涵盖WiFi指纹、采集位置坐标及其他相关信息。
每条WiFi指纹可通过检测到的无线接入点（Wireless Access Points, WAPs）及其对应的接收信号强度指示（Received Signal Strength Intensity, RSSI）进行表征。信号强度以负整数表示，取值范围为-104dBm（信号极差）至0dBm，当未检测到某WAP时，用正整数100标记。在数据集构建过程中，共检测到520个不同的WAP，因此每条WiFi指纹由520个信号强度值组成。
此外，数据集还提供了待预测的坐标信息：纬度、经度、楼层以及建筑ID。
额外采集的信息包括：数据采集所在的具体空间（办公室、实验室等）、采集点相对于该空间的位置（空间内/门外），其中“门外”指采集点位于目标空间的正门前。同时还记录了采集者（用户）、采集设备（安卓设备及其版本）以及采集时间戳等信息。

属性说明：
属性001（WAP001）：WAP001的信号强度值。取值为-104至0的负整数，未检测到WAP001时使用正整数100。
……
属性520（WAP520）：WAP520的信号强度值。取值为-104至0的负整数，未检测到WAP520时使用正整数100。
属性521（经度，Longitude）：经度坐标，负实数，取值范围为-7695.9387549299299000至-7299.786516730871000。
属性522（纬度，Latitude）：纬度坐标，正实数，取值范围为4864745.7450159714至4865017.3646842018。
属性523（楼层，Floor）：建筑内的楼层海拔（以楼层数计），整数取值范围为0至4。
属性524（建筑ID，BuildingID）：建筑识别编号，本次数据采集覆盖3栋不同建筑，为0至2的分类整数。
属性525（空间ID，SpaceID）：用于标识数据采集所在空间（办公室、走廊、教室等）的内部编号，分类整数。
属性526（相对位置，RelativePosition）：采集点相对于目标空间的位置标识（1=空间内，2=正门外），分类整数。
属性527（用户ID，UserID）：用户识别编号（详见下文），分类整数。
属性528（设备ID，PhoneID）：安卓设备识别编号（详见下文），分类整数。
属性529（时间戳，Timestamp）：数据采集时的UNIX时间，整数格式。

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用户ID与匿名用户身高（厘米）对照表
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UserID | 匿名用户标识 | 身高(cm)
0 | USER0000（验证用户） | N/A
1 | USER0001 | 170
2 | USER0002 | 176
3 | USER0003 | 172
4 | USER0004 | 174
5 | USER0005 | 184
6 | USER0006 | 180
7 | USER0007 | 160
8 | USER0008 | 176
9 | USER0009 | 177
10 | USER0010 | 186
11 | USER0011 | 176
12 | USER0012 | 158
13 | USER0013 | 174
14 | USER0014 | 173
15 | USER0015 | 174
16 | USER0016 | 171
17 | USER0017 | 166
18 | USER0018 | 162
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设备ID与安卓设备及版本对照表
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PhoneID | 安卓设备型号 | 安卓版本 | 对应UserID
0 | Celkon A27 | 4.0.4(6577) | 0
1 | GT-I8160 | 2.3.6 | 8
2 | GT-I8160 | 4.1.2 | 0
3 | GT-I9100 | 4.0.4 | 5
4 | GT-I9300 | 4.1.2 | 0
5 | GT-I9505 | 4.2.2 | 0
6 | GT-S5360 | 2.3.6 | 7
7 | GT-S6500 | 2.3.6 | 14
8 | Galaxy Nexus | 4.2.2 | 10
9 | Galaxy Nexus | 4.3 | 0
10 | HTC Desire HD | 2.3.5 | 18
11 | HTC One | 4.1.2 | 15
12 | HTC One | 4.2.2 | 0
13 | HTC Wildfire S | 2.3.5 | 0,11
14 | LT22i | 4.0.4 | 0,1,9,16
15 | LT22i | 4.1.2 | 0
16 | LT26i | 4.0.4 | 3
17 | M1005D | 4.0.4 | 13
18 | MT11i | 2.3.4 | 4
19 | Nexus 4 | 4.2.2 | 6
20 | Nexus 4 | 4.3 | 0
21 | Nexus S | 4.1.2 | 0
22 | Orange Monte Carlo | 2.3.5 | 17
23 | Transformer TF101 | 4.0.3 | 2
24 | bq Curie | 4.1.1 | 12
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相关论文：
Joaquín Torres-Sospedra, Raúl Montoliu, Adolfo Martínez-Usó, Tomar J. Arnau, Joan P. Avariento, Mauri Benedito-Bordonau, Joaquín Huerta. UJIIndoorLoc: A New Multi-building and Multi-floor Database for WLAN Fingerprint-based Indoor Localization Problems. 收录于第五届室内定位与室内导航国际会议论文集，2014年。可访问：<a href="http://www.ipi"

搜集汇总

背景与挑战

背景概述

UJIIndoorLoc数据集是一个用于室内定位的WiFi指纹数据库，包含19937条训练记录和1111条验证记录，覆盖三个建筑物和多个楼层。每条记录包含520个WiFi接入点的信号强度信息，以及地理位置坐标、楼层和建筑物ID等属性，适用于建筑物和楼层识别等分类任务。