DOI: 10.21227/mrkt-zr51
title: CRAWDAD copelabs/usense

description: Data concerning social interaction and propinquity based on wireless and bluetooth.This dataset comprises experiments carried out with the open-source middleware NSense (fomerly named as USense), available at https://github.com/COPELABS-SITI/NSense. The data has been collected based on four sensors: bluetooth; Wi-Fi; microphone; accelerometer. NSense then relies on four different pipelines to compute aspects such as relative distance (Wi-Fi); social strength (based on bluetooth contact duration); sound activity level; motion. We set up experiments making use of Samsung Galaxy S3 devices. For each experiment, there is the following set of data files: - SocialProximity.dat has three columns: Timestamp, DeviceName, Encounter Duration, Average Encounter Duration, Social Strength (Per hour) and Social Strength(Per minute) towards DeviceName - DistanceOutput.dat has three columns: Timestamp, DeviceName, and Distance towards DeviceName - Microphone.dat has two columns: Timestamp, and Soundlevel(QUIET, NORMAL, ALERT and NOISY) - PhysicalActivity.dat has two columns: Timestamp, and Activity as STATIONARY, WALKING and RUNNING There are two tracesets. A first traceset has been collected relying on a first NSense version in 2015. Then, a second traceset has been collected in 2016, with a refined version of NSense. In all tracesets, devices have been carried around by people that share the same affiliation during their individual daily routines (24 hour periods).date/time of measurement start: 2015-11-01date/time of measurement end: 2016-09-23collection environment:  This dataset comprises two tracesets collected in different years, for people carrying around Android smartphones with the open-source middleware installed. The devices were carried around by people during their daily routines (commuting between home and office, going to leisure activities, attending meetings in the office). Some people shared affiliation.network configuration: The tracesets were collected opportunistically. No internet access was required. date collection methodology: We set up experiments making use of Android smartphones (Android 4.2, Android 5.1). Each device had the open-source NSense middleware installed (https://github.com/COPELABS-SITI/NSense). The data was collected locally via NSense v1.0, and then aggregated. NSense v1.0 collects data based on 4 pipelines: motion (accelerometer); proximity (bluetooth); location (Wi-Fi); sound activity (microphone). For each traceset, we have carried several experiments. Then, on each experiment we have collected data for each device (1 folder per device) - sampled every minute. The source folder holds several .dat files: *SocialStrength.dat: Date, device Id, social strength * SocialStrength.dat has three columns: Timestamp, DeviceName, Encounter Duration, Average Encounter Duration, Social Strength (Per hour) and Social Strength(Per minute) towards DeviceName * Distance.dat has three columns: Timestamp, Destination Device Id, and Distance towards Destination Device Id. If -1: distance could not be computed. * Microphone.dat has two columns: Timestamp and Soundlevel(QUIET, NORMAL, ALERT and NOISY) * PhysicalActivity.dat has two columns: Timestamp, and Activity as STATIONARY, WALKING and RUNNING.limitation: The relative distance is being computed via a propagation loss model (Wi-Fi), as NSense considers non-intrusive measurement. Android does not allow RSSI based measurement, as RSSI is hard-coded (constant, equal to 60). Due to this, there were several cases detected where the distance could not be computed. For those cases, the distance value is -1: from our measurement, we have detected distance between 0 and 100 meters. "-1" allows the researchers to detect that this is an abnormal behavior.Tracesetscopelabs/usense/usenseSocial interaction experimentsfile:social-interaction.tar.gzdescription: We set up experiments making use of Samsung Galaxy S3 devices. For each experiment, there is the following set of data files: * SocialProximity.dat has three columns: Timestamp, DeviceName, Encounter Duration, Average Encounter Duration, Social Strength (Per hour) and Social Strength(Per minute) towards DeviceName * DistanceOutput.dat has three columns: Timestamp, DeviceName, and Distance towards DeviceName * Microphone.dat has two columns: Timestamp, and Soundlevel(QUIET, NORMAL, ALERT and NOISY) * PhysicalActivity.dat has two columns: Timestamp, and Activity as STATIONARY, WALKING and RUNNING For experiment 1, Experiment was conducted for the period of 22 hours. Among the four devices, two devices were placed in the lab, and other two devices were carried by users. The intention was to collect the data when the two devices are in close contact and the other two devices with daily routines. For experiment 2, Second experiment was conducted for the period of 50 hours. All the devices are following the daily routines. For experiment 3, Third experiment was conducted for 22 hours. Devices were following their daily routine with two periods (one of X hours and the other of Y hours) where all devices left the office and moved randomly around the office area. The intention was to capture a dynamic variability of the collected data.measurement purpose: Educational Use, Social Network Analysi, Human Behavior Modeling, Localization, Opportunistic Connectivitymethodology: This data set comprises experiments carried out considering four Android devices, each named Usense 2, 3, 4, and 5, respectively. These devices were carried by people sharing the same affiliation during their daily routines (commuting between home and office, going to leisure activities, attending meetings in the office). Aall the data was collected each and every one minute. It contains 3 different traces (Experiment I, Experiment II, Experiment III)