CRAWDAD yonsei/lifemap

We deployed our mobility monitoring system, called LifeMap, to collect fine-grained mobility data from commercial mobile phones over two months in Seoul, Korea. The dataset contains location information (latitude and longitude) with accuracy (error bound), Wi-Fi fingerprints (MAC address and signal strength of surrounding Wi-Fi APs), user-defined types of places (workplace, cafeteria, etc.). Our system continuously collected this information every 2 to 5 minutes for everyday location monitoring.last modified : 2012-05-08release date : 2012-01-03date/time of measurement start : 2011-03-08date/time of measurement end : 2011-11-16collection environment : The data was collected in Seoul, Korea from 8 graduate students of Yonsei University. In the collected data traces, there are 651 meaningful places, 9681 nodes on 1717 paths, and 52510 WiFi APs.network configuration : We implemented SmartDC on the Android SDK 2.1 running on commercial mobile phones equipped with GPS, GSM/CDMA, and Wi-Fi. The application LifeMap provides a user interface for SmartDC; it is available in Android Market.data collection methodology : We collected real traces from 8 graduate students over four weeks using HTC Hero, HTC Desire, and Samsung Galaxy S smartphones. SmartDC was running as a background service to automatically collect the user's mobility and to trace sensor usage time. Participants installed SmartDC on their primary phones. To collect the ground truth, the participants explicitly labeled the place names and kept a diary of places they had visited with the entrance and departure times.sanitization : We removed personal information such as user names and users' MAC addresses. User-labelled place names are anonymized with one letter of alphabet (A, B, C, and so on) and number (from 001 to 999). We anonymized MAC addresses of APs by changing the second and the fifth parts of the MAC address into randomized characters (e.g., 00:00:00:00:00:00 is changed into 00:xx:00:00:xx:00). The uniqueness of APs is maintained: the same address indicates the same AP.limitation : The users spent some of their time in regions without Wi-Fi coverage.Tracesetyonsei/lifemap/mobilityMobility data collected by LifeMap monitoring system at Yonsei University in Seoul.description: We deployed our mobility monitoring system, called LifeMap, to collect fine-grained mobility data from commercial mobile phones over two months in Seoul, Korea. The dataset contains location information (latitude and longitude) with accuracy (error bound), Wi-Fi fingerprints (MAC address and signal strength of surrounding Wi-Fi APs), user-defined types of places (workplace, cafeteria, etc.). Our system continuously collected this information every 2 to 5 minutes for everyday location monitoring.measurement purpose: User Mobility Characterization, Energy-efficient Wireless Network, Human Behavior Modelingmethodology: The first level of sensing uses GSM to obtain the Location Area Code (LAC) to detect exceptions within the predicted sensing schedule. The first level continuously monitors the LAC with minor energy consumption, since a mobile phone basically updates the LAC for voice communication. The system does not activate the second level until the next sensing time, if the observed LAC follows a predicted sequence-pattern. Otherwise, if the first level detects an exception, the system immediately uses the second level to collect a new pattern of individual mobility. For example, if a user normally goes to the office on weekday mornings, SmartDC only turns on Wi-Fi near the entrance of the office if the expected LAC is observed. If a user goes on a business trip, the system uses the mobility learner when it detects a new pattern of LAC. The second level uses Wi-Fi scanning to recognize a change of places and revisited places. The basic operation is that if a user is stationary, the signal fingerprints of surrounding Wi-Fi APs are relatively similar to each other. We use a scan window to perform multiple scans to tolerate noisy signals. The system generates meaningful places when it detects the stationary state. When a user revisits a place, the system aggregates mobility data and reuses physical location information without activating additional sensors. The second level also uses wireless communication to obtain coarse location from the WPS provided by Android. The third level activates GPS to acquire fine location, if the system fails to get accurate location in the second level. The time interval used for data collection is set to two minutes. The Wi-Fi scanning intervals and window size are 10 seconds and 30 seconds respectively, and the similarity threshold of the Wi-Fi vector is set to 0.7. The accuracy threshold for the WPS is set to 500 meters. The GPS is activated for 30 seconds for single positioning, which is common in GPS usage. To reduce computation overheads in adaptive duty cycling, we (1) converted the float value to an integer value with 10-3 precision, (2) used discrete time intervals in minutes, and (3) scaled down the energy budget, dividing it by the energy consumption of Wi-Fi scanning, which is the minimum cost in our scheme. We set the energy budget E and the sensing cost c as follows: The maximum energy budget is (1,400 mA×3.7 V34.5 mW)×3,600 s=18.5 kJ, which is the available battery capacity excluding the energy cost of idle state. If the battery level and energy constraints are 30% and 10%, the allowed energy budget is 18.5 kJ×0.3×0.1=555 J. The level 2 sensing cost is the energy consumption of Wi-Fi scanning: 114.5 mW×30 s=3.5 J. The level 3 sensing consumes 3.5 J+440.8 mW×30 s=16.7 J: the energy of level-2 sensing and reading GPS for 30 seconds.sanitization: We removed personal information such as user names and users' MAC addresses. User-labelled place names are anonymized with one letter of alphabet (A, B, C, and so on) and number (from 001 to 999). We anonymized MAC addresses of APs by changing the second and the fifth parts of the MAC address into randomized characters (e.g., 00:00:00:00:00:00 is changed into 00:xx:00:00:xx:00). The uniqueness of APs is maintained: the same address indicates the same AP.yonsei/lifemap/mobility Traces2011: Mobility data collected by LifeMap monitoring system at Yonsei University in Seoul.configuration: The Wi-Fi scanning intervals and window size are 10 seconds and 30 seconds respectively, and the similarity threshold of the Wi-Fi vector is set to 0.7. The accuracy threshold for the WPS is set to 500 meters. The GPS is activated for 30 seconds for single positioning, which is common in GPS usage.format:The trace contains the following files.* README.txtThe file describes basic information (our affiliation, published paper, etc.)and data description.* DB_Schema.mwb, DB_Schema.pngDatabase schema using MySQL Workbench(http://www.mysql.com/downloads/workbench/).For each user, the trace contains the file * LifeMap_GS.dbsqlite Database file exported from Android platform. * example_codeEclipse project folder for managing dataset.#########################################How to extract mobility data from dataset#########################################We recommend using stayTable for extracting mobility traces instead ofedgeTable.We provided the example of Java source code to handle database file. The sourcecode can be imported as project in Eclipse.See showMobilityTrace() in LifeMapDatabase.java.To run example code in Eclipse,1. Copy our dataset and sqlite-jdbc-xxx.jar (http://code.google.com/p/sqlite-jdbc/)into project paths.2. Run Eclipse and import project.3. Set sqlite-jdbc-xxx.jar to library of projects.4. Run Java application in Main.java.####################################################How to import database file into Android application####################################################1. Copy database file into your Android device.2. download and install 'LifeMap' application from Android market(https://market.android.com/details?id=com.mobed.lifemap).3. Click sensond tab, and open 'Menu' -> 'Manage Space' -> 'Load Database.'4. Type the path of database file name (e.g., /sdcard/data/LifeMap_GS1.db).5. Restart 'LifeMap' application.######################Desription of data set######################------------------------------------------------------------------------| User + Sex + Age + Number of Place + Periods + Start Date + End Date |------------------------------------------------------------------------| GS1 + Male + 20s + 92 + 134 + 2011.3.8 + 2011.7.19 || GS2 + Male + 20s + 163 + 198 + 2011.5.2 + 2011.11.15 || GS3 + Male + 20s + + 297 + 2010.11.17 + 2011.8.14 || GS4 + Female + 20s + 209 + 183 + 2011.5.7 + 2011.11.15 || GS7 + Male + 20s + 289 + 132 + 2011.3.10 + 2011.7.19 || GS8 + Male + 30s + 345 + 250 + 2011.3.10 + 2011.11.14 || GS9 + Male + 30s + 198 + 193 + 2011.3.26 + 2011.10.4 || GS10 + Male + 20s + 87 + 58 + 2011.9.30 + 2011.11.16 || GS12 + Male + 20s + 376 + 366 + 2010.11.14 + 2011.11.14 |--------------------------------------------------------##########################Description of data values##########################The timestamp is stored as yyyyMMddHHmmssEEE, e.g., 20110509030752TUE means2011-05-09 03:07:52 Tuesday.# table name## column name### comments# locationTable## _latitude, _longitude, _latitude_gps, _longitude_gps, _latitude_wifi,## _longitude_wifi### latitude and longitude on Earth with 10^(-6) precision.### e.g., data value (123456789) means latitude or longtitude (123.456789) as### degree value.### _gps indicates data from GPS, _wifi indicates data from WPS,### _latitude/_longitude is the better one out of GPS or WPS data.### 0 = UNKNOWN_LOCATION## _accuracy, _accuracy_gps, _accuracy_wifi### error bound of location provided by Android in meters.### 1 = user manually changed location information### 100000 = UNKNOWN_ACCURACY## _activity### 1 = STATIONAY### 2 = MOVE## _place_name### user-labelled place name. The data value is anonymized with one letter of### alphabet (e.g., A, B, C, and so on) and number (e.g., from 001 to 999).## _time_location### latest saved date as timestamp# apTable## _bssid### anonymized MAC address.### The uniqueness is maintained (i.e., same MAC addresses indicates same APs### in entire dataset).## _signal### average value of recieved signal strength## _signal_deviation### deviation of recieved signal strengths## _time_ap### latest saved date as timestamp# cellTable## _cell_type### data from android.telephony.TelephonyManger.getPhoneType()### see http://developer.android.com/reference/android/telephony/TelephonyManager.html#getPhoneType()### 0 = PHONE_TYPE_NONE### 1 = PHONE_TYPE_GSM### 2 = PHONE_TYPE_CDMA### 3 = PHONE_TYPE_SIP## _connect_time### connection duration as milliseconds.### the data is under-estimated since the system collected it in short burst.## _time_cell### latest saved date as timestamp# stayTable## _stay_time### stay duration as milliseconds## _stay_start_time### start time (arrival time) of stay behavior as timestamp## _time_stay### end time (departure time) of stay behavior as timestamp# batteryTable## _battery_level### Extra for ACTION_BATTERY_CHANGED: integer field containing the current battery level, from 0 to EXTRA_SCALE.### see http://developer.android.com/reference/android/os/BatteryManager.html#EXTRA_LEVEL## _battery_status### Extra for ACTION_BATTERY_CHANGED: integer containing the current status constant.### see http://developer.android.com/reference/android/os/BatteryManager.html#EXTRA_STATUS### 1=BATTERY_STATUS_UNKNOWN, 2=BATTERY_STATUS_CHARGING, 3=BATTERY_STATUS_DISCHARGING, 4=BATTERY_STATUS_NOT_CHARGING, 5=BATTERY_STATUS_FULL## _battery_voltage### Extra for ACTION_BATTERY_CHANGED: integer containing the current battery voltage level.### see http://developer.android.com/reference/android/os/BatteryManager.html#EXTRA_VOLTAGE## _time_battery### saved date as timestamp

我们部署了名为LifeMap的移动性监测系统，在韩国首尔的商用移动设备上历时两个月，采集细粒度的移动性数据。本数据集包含带有精度（误差范围）的位置信息（纬度与经度）、Wi-Fi指纹（周边Wi-Fi接入点的媒体访问控制地址 (Media Access Control Address, MAC) 与信号强度），以及用户自定义的场所类型（如工作场所、自助餐厅等）。我们的系统每2至5分钟持续采集此类信息，用于日常的位置监测。
最后修改时间：2012-05-08；发布日期：2012-01-03；测量开始时间：2011-03-08；测量结束时间：2011-11-16
采集环境：数据采集于韩国首尔，对象为延世大学的8名研究生。采集到的数据轨迹中包含651个有效场所、1717条路径上的9681个轨迹节点，以及52510个Wi-Fi接入点。
网络配置：我们在搭载全球定位系统 (GPS)、全球移动通信系统/码分多址 (GSM/CDMA) 与Wi-Fi的商用移动设备上，基于安卓 (Android) SDK 2.1实现了SmartDC。LifeMap应用为SmartDC提供了用户界面，该应用可在安卓应用商店 (Android Market) 获取。
数据采集方法论：我们使用HTC Hero、HTC Desire与三星Galaxy S三款智能手机，从8名研究生处采集真实轨迹，历时四周。SmartDC作为后台服务运行，自动采集用户的移动性信息并追踪传感器使用时长。参与者将SmartDC安装在其主力手机上。为获取地面真值，参与者需明确标注场所名称，并记录到访场所的出入时间日志。
数据脱敏：我们已移除用户名、用户MAC地址等个人信息。用户标注的场所名称通过字母（如A、B、C等）与数字（001至999）进行匿名化处理。我们将接入点的MAC地址的第二与第五段替换为随机字符以实现匿名化（例如，00:00:00:00:00:00将被改为00:xx:00:00:xx:00），同时保留接入点的唯一性：相同的地址对应同一个接入点。
局限性：部分用户曾在无Wi-Fi覆盖的区域停留。
数据集标识：yonsei/lifemap/mobility；由首尔延世大学的LifeMap监测系统采集的移动性数据。
描述：我们部署了名为LifeMap的移动性监测系统，在韩国首尔的商用移动设备上历时两个月，采集细粒度的移动性数据。本数据集包含带有精度（误差范围）的位置信息（纬度与经度）、Wi-Fi指纹（周边Wi-Fi接入点的MAC地址与信号强度），以及用户自定义的场所类型（如工作场所、自助餐厅等）。我们的系统每2至5分钟持续采集此类信息，用于日常的位置监测。
测量目的：用户移动性特征刻画、节能无线网络、人类行为建模
采集方法论：第一级感知采用GSM获取位置区域码（Location Area Code, LAC），以检测预测感知调度内的异常情况。由于移动设备通常会为语音通信更新LAC，因此第一级以极低的能耗持续监测LAC。若观测到的LAC符合预测的序列模式，系统将不启动第二级感知，直至下一次感知时刻；反之，若第一级检测到异常，系统将立即启动第二级感知，以采集个体移动的新模式。例如，若用户通常在工作日早上去办公室，当观测到预期的LAC时，SmartDC仅会在办公室入口附近开启Wi-Fi扫描。若用户出差，系统在检测到LAC的新模式时，将启动移动性学习模块。第二级感知采用Wi-Fi扫描来识别场所变化与重访场所。其基本逻辑为：若用户处于静止状态，周边Wi-Fi接入点的信号指纹会相对相似。我们使用扫描窗口执行多次扫描以抵消信号噪声。当系统检测到静止状态时，将生成有效场所。当用户重访某一场所时，系统会聚合移动性数据并复用物理位置信息，无需启动额外传感器。第二级感知还通过无线通信获取安卓提供的WPS粗粒度位置信息。若第二级无法获取精确位置，第三级将启动GPS以采集精细位置信息。数据采集的时间间隔设置为2分钟。Wi-Fi扫描间隔与扫描窗口大小分别为10秒与30秒，Wi-Fi向量的相似度阈值设置为0.7。WPS的精度阈值设置为500米。单次GPS定位会启动GPS 30秒，这是GPS使用的常规操作。为降低自适应占空比循环中的计算开销，我们采取了三项优化：(1) 将浮点值转换为精度为10^-3的整数值；(2) 使用以分钟为单位的离散时间间隔；(3) 缩减能源预算，将其除以Wi-Fi扫描的能耗（本方案中的最低能耗）。我们设置能源预算E与感知成本c如下：最大能源预算为(1,400 mA×3.7 V×34.5 mW)×3,600 s=18.5 kJ，该值为扣除空闲状态能耗后的可用电池容量。若电池电量与能源约束分别为30%与10%，则允许的能源预算为18.5 kJ×0.3×0.1=555 J。第二级感知成本为Wi-Fi扫描的能耗：114.5 mW×30 s=3.5 J。第三级感知的能耗为3.5 J+440.8 mW×30 s=16.7 J，其中包含第二级感知能耗与30秒GPS读取能耗。
数据脱敏：我们已移除用户名、用户MAC地址等个人信息。用户标注的场所名称通过字母（如A、B、C等）与数字（001至999）进行匿名化处理。我们将接入点的MAC地址的第二与第五段替换为随机字符以实现匿名化（例如，00:00:00:00:00:00将被改为00:xx:00:00:xx:00），同时保留接入点的唯一性：相同的地址对应同一个接入点。
配置：Wi-Fi扫描间隔与扫描窗口大小分别为10秒与30秒，Wi-Fi向量的相似度阈值设置为0.7。WPS的精度阈值设置为500米。单次GPS定位会启动GPS 30秒，这是GPS使用的常规操作。
数据格式：轨迹包含以下文件：
* README.txt：该文件描述了基本信息（我们的所属机构、已发表论文等）与数据说明。
* DB_Schema.mwb、DB_Schema.png：使用MySQL Workbench（http://www.mysql.com/downloads/workbench/）制作的数据库架构图。
针对每个用户，轨迹包含以下文件：
* LifeMap_GS.dbsqlite：从安卓平台导出的数据库文件。
* example_code：用于管理数据集的Eclipse项目文件夹。
#########################################
如何从数据集中提取移动性数据
#########################################
我们推荐使用stayTable而非edgeTable来提取移动性轨迹。我们提供了用于处理数据库文件的Java源代码示例。该源代码可作为项目导入Eclipse，详见LifeMapDatabase.java中的showMobilityTrace()方法。
若需在Eclipse中运行示例代码，请按以下步骤操作：
1. 将本数据集与sqlite-jdbc-xxx.jar（http://code.google.com/p/sqlite-jdbc/）复制到项目路径中。
2. 启动Eclipse并导入项目。
3. 将sqlite-jdbc-xxx.jar设置为项目的库文件。
4. 在Main.java中运行Java应用程序。
####################################################
如何将数据库文件导入安卓应用程序
####################################################
1. 将数据库文件复制到您的安卓设备中。
2. 从安卓应用商店（https://market.android.com/details?id=com.mobed.lifemap）下载并安装“LifeMap”应用程序。
3. 点击传感器标签页，打开“菜单”->“管理空间”->“加载数据库”。
4. 输入数据库文件的路径（例如，/sdcard/data/LifeMap_GS1.db）。
5. 重启“LifeMap”应用程序。
######################
数据集说明
######################
------------------------------------------------------------------------
| 用户编号 + 性别 + 年龄区间 + 场所数量 + 轨迹时段数 + 开始日期 + 结束日期 |
------------------------------------------------------------------------
| GS1 + 男 + 20多岁 + 92 + 134 + 2011.3.8 + 2011.7.19 |
| GS2 + 男 + 20多岁 + 163 + 198 + 2011.5.2 + 2011.11.15 |
| GS3 + 男 + 20多岁 + 无记录 + 297 + 2010.11.17 + 2011.8.14 |
| GS4 + 女 + 20多岁 + 209 + 183 + 2011.5.7 + 2011.11.15 |
| GS7 + 男 + 20多岁 + 289 + 132 + 2011.3.10 + 2011.7.19 |
| GS8 + 男 + 30多岁 + 345 + 250 + 2011.3.10 + 2011.11.14 |
| GS9 + 男 + 30多岁 + 198 + 193 + 2011.3.26 + 2011.10.4 |
| GS10 + 男 + 20多岁 + 87 + 58 + 2011.9.30 + 2011.11.16 |
| GS12 + 男 + 20多岁 + 376 + 366 + 2010.11.14 + 2011.11.14 |
--------------------------------------------------------
##########################
数据值说明
##########################
时间戳以yyyyMMddHHmmssEEE格式存储，例如20110509030752TUE代表2011-05-09 03:07:52 星期二。

# 表名
## 列名
### 注释
# locationTable
## _latitude, _longitude, _latitude_gps, _longitude_gps, _latitude_wifi,
## _longitude_wifi
### 地球坐标系下的纬度与经度，精度为10^(-6)。例如，数值(123456789)代表纬度或经度为123.456789度。
### _gps表示来自GPS的数据，_wifi表示来自WPS的数据，_latitude/_longitude为GPS与WPS数据中更优的结果。
### 0 = 未知位置（UNKNOWN_LOCATION）
# _accuracy, _accuracy_gps, _accuracy_wifi
### 安卓系统提供的位置误差范围，单位为米。
### 1 = 用户手动修改的位置信息
### 100000 = 未知精度（UNKNOWN_ACCURACY）
# _activity
### 1 = 静止（STATIONARY）
### 2 = 移动（MOVE）
# _place_name
### 用户标注的场所名称。数据值通过字母（如A、B、C等）与数字（如001至999）进行匿名化处理。
# _time_location
### 最新保存的时间戳
# apTable
## _bssid
### 匿名化后的MAC地址。保留唯一性（即整个数据集中相同的MAC地址对应同一个接入点）。
## _signal
### 接收信号强度的平均值
## _signal_deviation
### 接收信号强度的标准差
## _time_ap
### 最新保存的时间戳
# cellTable
## _cell_type
### 来自android.telephony.TelephonyManager.getPhoneType()的数据。详见http://developer.android.com/reference/android/telephony/TelephonyManager.html#getPhoneType()
### 0 = PHONE_TYPE_NONE
### 1 = PHONE_TYPE_GSM
### 2 = PHONE_TYPE_CDMA
### 3 = PHONE_TYPE_SIP
## _connect_time
### 连接时长，单位为毫秒。由于系统以短突发方式采集数据，该数值存在低估。
## _time_cell
### 最新保存的时间戳
# stayTable
## _stay_time
### 停留时长，单位为毫秒
## _stay_start_time
### 停留行为的开始时间（到达时间），以时间戳表示
## _time_stay
### 停留行为的结束时间（离开时间），以时间戳表示
# batteryTable
## _battery_level
### ACTION_BATTERY_CHANGED的额外字段：包含当前电池电量的整数值，范围为0至EXTRA_SCALE。详见http://developer.android.com/reference/android/os/BatteryManager.html#EXTRA_LEVEL
## _battery_status
### ACTION_BATTERY_CHANGED的额外字段：包含当前状态常量的整数值。详见http://developer.android.com/reference/android/os/BatteryManager.html#EXTRA_STATUS
### 1=BATTERY_STATUS_UNKNOWN（未知状态）, 2=BATTERY_STATUS_CHARGING（充电中）, 3=BATTERY_STATUS_DISCHARGING（放电中）, 4=BATTERY_STATUS_NOT_CHARGING（未充电）, 5=BATTERY_STATUS_FULL（电量已满）
## _battery_voltage
### ACTION_BATTERY_CHANGED的额外字段：包含当前电池电压的整数值。详见http://developer.android.com/reference/android/os/BatteryManager.html#EXTRA_VOLTAGE
## _time_battery
### 保存的时间戳