CRAWDAD unical/socialblueconn

The dataset contains Bluetooth device proximity data collected by an ad-hoc Android application called SocialBlueConn. This application was used by 15 students at University of Calabria campus in Rende, Cosenza (Italy) and logged both the internal contacts between the participants and contacts with other 20 external mobile nodes. The dataset includes the social profiles (Facebook friends and self-declared interests) of the participants. date/time of measurement start: 2014-01-28date/time of measurement end: 2014-02-05 collection environment: Experimental data were collected at the campus of University of Calabria in Rende (CS), Italy. 35 students were gathered in order to form the experiment team. After the presentation of the project, 15 students decided to participate to the experiment. During the experiment's lifetime, the 15 students detected also 20 external devices. Hence, the Bluetooth proximity data are relative to a total number of 35 devices. The experiment lasted one week during student's lessons, from January 28, 2014 to February 5, 2014, including only the working days.network configuration: The network is a Bluetooth-based opportunistic network created among the participating devices. Each device performs a periodic Bluetooth device discovery every 180 seconds to find out about nearby devices. The smartphone's hardware of each device is different, with a Bluetooth range of about 10 meters; the operating system for all devices is Android.data collection methodology: In order to gather the proximity information, an ad hoc application called SocialBlueConn was installed on each student's smartphone. Each participant was instructed to keep the device with himself and powered on from 12 a.m. to 20 p.m., and to use the application for mobile social networking during the experiment. Each device recorded the results of the periodic device discovery; all the wireless contacts were recorded in a text file on the device's SD. Each contact has a timestamp based on the device clock and reported as a relative time in milliseconds, referring to Thu Jan 01 1970 01:00:00 GMT 0100, the start of Unix Epoch Time. In order to obtain the Facebook friendships, the participants were asked to login with their Facebook credentials to an ad-hoc website accessing Facebook API. Once the students logged in, their friend lists and their social profiles were collected and sent to a central server. Participants' interests were collected at the beginning of the experiment through an offline questionnaire. The questionnaire contained a list of questions regarding participants' preferences according to the following macro-categories: (a) mobility, (b) sport, (c) music, (d) cinema, (e) literature, (f) multimedia entertainment, (g) politics, (h) other hobbies and (i) social networks. For each category, students could choose one or more sub-categories.sanitization: Sensitive identifiers as Facebook identifiers and Bluetooth MAC addresses were replaced with random integer IDs.limitation: The Bluetooth data communications suffer from the known limitations of the Bluetooth technology. The device discovery process was slow and regularly missed some nearby devices; links often failed when there were many Bluetooth devices in range. The timestamps among different devices are not synchronized. The clocks have been set manually to the same reference time at the beginning of the experiment.Tracesetunical/socialblueconnfile: contacts.zip, friendships.zip, interests.zipdescription: The dataset contains Bluetooth device proximity data collected by an ad-hoc Android application called SocialBlueConn. This application was used by 15 students at University of Calabria campus in Rende, Cosenza (Italy) and logged both the internal contacts between the participants and contacts with other 20 external mobile nodes. The dataset includes the social profiles (Facebook friends and self-declared interests) of the participants.measurement purpose: User Mobility Characterization, Social Network Analysis, Human Behavior Modeling, Opportunistic Connectivitymethodology: Experimental data were collected at the campus of University of Calabria in Rende (CS), Italy. 35 students were gathered in order to form the experiment team. After the presentation of the project, 15 students decided to participate to the experiment. During the experiment's lifetime, the 15 students detected also 20 external devices. Hence, the Bluetooth proximity data are relative to a total number of 35 devices. The experiment lasted one week during student's lessons, from January 28, 2014 to February 5, 2014, including only the working days.unical/socialblueconn Traces contacts: The Bluetooth contacts logsconfiguration: The trace records all the nearby Bluetooth devices reported by the periodic Bluetooth device discoveries.format: txt: ID1; ID2; TimestampID1 represents the source device and ID2 the destination device. The timestamp is based on the device clock and reported as a relative time in milliseconds, referring to Thu Jan 01 1970 01:00:00 GMT 0100, the start of Unix Epoch Time. Bluetooth device discovery is asymmetric; a device A may see device B at some point in time but not the other way around.friendships: Facebook friendships between the participantsconfiguration: Using Facebook API, we developed an application collecting the friend lists of each candidate.format: txt: In the file there is a square matrix, where the 15 nodes of the network are represented on the first row and first column. We define the existence of a friendship between nodes A and B through the presence of the value 1 on the corresponding position in the matrix. Due to the fact that the friendship relation is symmetric on Facebook, the matrix is symmetric.interests: Self-declared interests of the participants over different categoriesconfiguration: Participants' interests were collected at the beginning of the experiment through an offline questionnaire. The questionnaire contained a list of questions regarding participants' preferences according to the following macro-categories: (a) mobility, (b) sport, (c) music, (d) cinema, (e) literature, (f) multimedia entertainment, (g) politics, (h) other hobbies and (i) social networks. For each category, students could choose one or more sub-categories.format: txt: There are 10 .txt files.Interest_legend: it contains a legend in which the associations between the IDs and the major categories and subcategories are specified. Interest_MacrocategoryX (where X varies from A to I): it contains a matrix where on the first row there is the reference to the macrocategory, on the second row the references to the subcategories and on the first column the 15 nodes' IDs. We define the node's choice of a specific category and subcategory through the presence of the value 1 on the corresponding position in the matrix.

本数据集包含由一款名为SocialBlueConn的临时安卓（Android）应用采集的蓝牙（Bluetooth）设备邻近数据。该应用由意大利科森扎省伦代市卡拉布里亚大学校园内的15名学生使用，记录了参与者之间的内部接触情况，以及与另外20个外部移动节点的接触记录。数据集还包含参与者的社交档案（Facebook好友及自报兴趣）。

测量开始时间：2014-01-28；测量结束时间：2014-02-05。
采集环境：实验数据采集于意大利伦代市卡拉布里亚大学校园。实验团队最初召集了35名学生，在项目介绍后，共有15名学生决定参与实验。在实验周期内，这15名学生还检测到了20台外部设备，因此本数据集的蓝牙邻近数据共涉及35台设备。实验于学生上课时段的一周工作日内开展，即2014年1月28日至2014年2月5日。

网络配置：该网络是在参与设备之间构建的基于蓝牙（Bluetooth）的机会网络。每台设备每180秒执行一次周期性蓝牙设备扫描，以发现附近设备。各设备的智能手机硬件配置存在差异，蓝牙覆盖范围约为10米；所有设备的操作系统均为安卓（Android）。

数据采集方法：为采集邻近信息，每名学生的智能手机上均安装了名为SocialBlueConn的临时应用。每位参与者需将设备随身携带并保持开机状态，开机时段为每日12:00至20:00，并在实验期间使用该应用进行移动社交网络交互。每台设备会记录周期性设备扫描的结果，所有无线接触记录都会存储在设备SD卡的文本文件中。每条接触记录均带有基于设备时钟的时间戳，以相对于1970年1月1日星期四01:00:00 GMT+0100（Unix纪元时间起点）的毫秒数表示。为获取Facebook好友关系，参与者需使用自己的Facebook凭据登录专用网站，该网站通过Facebook应用程序编程接口（Facebook API）获取数据。学生登录后，其好友列表及社交档案将被采集并发送至中央服务器。参与者的兴趣信息在实验开始时通过线下问卷收集。问卷包含针对以下大类的偏好问题：(a) 移动性、(b) 体育、(c) 音乐、(d) 电影、(e) 文学、(f) 多媒体娱乐、(g) 政治、(h) 其他爱好、(i) 社交网络。每个类别下，学生可选择一个或多个子类别。

数据清洗：敏感标识符（如Facebook标识符及媒体访问控制（MAC）地址）已替换为随机整数ID。

局限性：蓝牙数据通信存在该技术固有的局限性。设备扫描进程缓慢，时常会遗漏附近设备；当范围内存在大量蓝牙设备时，连接常会失败。不同设备之间的时间戳未同步，实验开始时已手动将所有设备时钟调整至同一参考时间。

数据集文件路径：Tracesetunical/socialblueconn，包含文件：contacts.zip、friendships.zip、interests.zip。

数据集描述：本数据集包含由一款名为SocialBlueConn的临时安卓（Android）应用采集的蓝牙（Bluetooth）设备邻近数据。该应用由意大利科森扎省伦代市卡拉布里亚大学校园内的15名学生使用，记录了参与者之间的内部接触情况，以及与另外20个外部移动节点的接触记录。数据集还包含参与者的社交档案（Facebook好友及自报兴趣）。

测量目标：用户移动性特征刻画、社交网络分析、人类行为建模、机会连接性研究。

采集环境：同前文所述，实验数据采集于意大利伦代市卡拉布里亚大学校园，实验周期为2014年1月28日至2014年2月5日的工作日周。

蓝牙接触轨迹数据（contacts）：该轨迹记录了周期性蓝牙设备扫描上报的所有附近蓝牙设备信息。格式：文本文件（.txt），内容格式为：ID1; ID2; 时间戳。其中ID1代表源设备，ID2代表目标设备。时间戳基于设备时钟，以相对于1970年1月1日星期四01:00:00 GMT+0100（Unix纪元时间起点）的毫秒数表示。蓝牙设备扫描具有非对称性：设备A可能在某一时刻检测到设备B，但反之则未必。

Facebook好友关系数据（friendships）：该数据记录了参与者之间的Facebook好友关系。配置：通过Facebook API开发的应用，采集每位参与者的好友列表。格式：文本文件（.txt），内容为方阵。网络中的15个节点分别对应方阵的首行与首列。若节点A与节点B之间存在好友关系，则方阵对应位置的数值为1。由于Facebook中的好友关系具有对称性，因此该方阵为对称矩阵。

参与者自报兴趣数据（interests）：该数据包含参与者自报的多类别兴趣信息。配置：参与者的兴趣信息在实验开始时通过线下问卷收集。问卷包含针对以下大类的偏好问题：(a) 移动性、(b) 体育、(c) 音乐、(d) 电影、(e) 文学、(f) 多媒体娱乐、(g) 政治、(h) 其他爱好、(i) 社交网络。每个类别下，学生可选择一个或多个子类别。格式：共10个文本文件（.txt）：
1. Interest_legend：包含图例，说明ID与大类及子类的对应关系；
2. Interest_MacrocategoryX（X从A到I）：包含方阵，首行为对应大类的标识，第二行为子类的标识，首列为15个节点的ID。若节点选择了某一特定大类及子类，则方阵对应位置的数值为1。