CRAWDAD strath/nodobo

Dataset of mobile phone usage records collected with Nodobo suite at the University of Strathclyde.

Dataset gathered by Nodobo, a suite of social sensor software for Android phones, during a study of the mobile phone usage at University of Strathclyde.

date/time of measurement start: 2010-09-09 

date/time of measurement end: 2011-02-23 

collection environment: Our researchers developed "Nodobo", a set of software extensions to the Google Android operating system, for enabling the capture and replay of smartphone user interactions sessions. The software captures a variety of social context data, including logs of phone calls, text messages, Bluetooth proximity detection, WiFi access point, and cell tower ID. The directionality of calls and text messages are recorded, along with the associated phone number, and the duration of the call or length of the message. Bluetooth proximity is detected every minute, and includes all devices in the study as well as any other clients which respond to service discovery. Basic positioning is achieved through WiFi hotspot and cell tower ID records. 

network configuration: Each of the study participants was given a Google Nexus One smartphone, prepared with a modified Android operating system. Data is stored in a simple database on the device SD card, which is then synchronised over the air to a central server. 

data collection methodology: The dataset was collected through monitoring devices of 27 users over a 5-month study. 

sanitization: Record fields containing personally identifiable information have been anonymised.  

Nodobo-2011-01-v1 is the traceset gathered by Nodobo software at University

of Strathclyde from September 2010 to February 2011.

Traceset

strath/nodobo/mobile

Traceset of mobile phone usage records collected with Nodobo suite at the University of Strathclyde.

• file: nodobo-release.tar.gz, nodobo-csv.zip
• description: Nodobo-2011-01-v1 is the traceset gathered by Nodobo software at University of Strathclyde from September 2010 to February 2011.
• measurement purpose: Usage Characterization, Social Network Analysis
• methodology: A group of 27 promising high school students in a Scottish state high school were selected for this study. All students previously had a mobile phone, with approximately 1/3 of these falling in the category of smartphone (iPhone, Blackberry, or similarly powerful handset). Each of the study participants was given a Google Nexus One smartphone, prepared with a modified Android operating system.

  The close proximity of the deployment to University of Strathclyde enables the study organisers to schedule regular visits to diagnose issues, as well as facilitating regular backups to be made. To maintain as up-to-date a dataset as possible, and to limit the number of visits required, the devices also synchronise with a web server over the mobile network or WiFi.

• sanitization: Record fields containing personally identifiable information have been anonymised.  

trath/nodobo/mobile Trace 

• social: Mobile phone usage records collected with Nodobo suite at University of Strathclyde in 2010-2011.
• configuration: 27 Google Nexus One smartphones were prepared with a modified Android operating system, running Nodobo. The phone database is synchronised periodically over-the-air with a web services data store. 
• format:  db.sqlite3.dump.bz2 is a bzipped SQL dump of the sqlite3 database. You can recreate the database by doing the following: 

bzcat db.sqlite3.dump.bz2 | sqlite3 db.sqlite3 

# Database schema

The following tables are used:

## Calls and Messages

* other_id: id of the other user on the call (NULL if not in the study)

* number: phone number of the other end of the call/message (related: 

Users#number)

* duration: length of the call in seconds

* length: number of characters in the message

## CellTowers

* cellid: GSM base transceiver station CID

* lac: location area code

## Devices

* imei: blank for this release of the data

* mac: Bluetooth MAC (related: Presences#mac)

## Presences

* other_id: user_id of the detected device (NULL if not in the study)

* mac: Bluetooth MAC (related: Devices#mac)

* bluetooth_class: reported class of the device

* name: human-readable name of the device

## Users

* name: "Anonymous" for this release of the data

* number: phone number of the study user (related: Calls#number, 

Messages#number)

## Wifis

* ssid: human-readable name of the base station

* bssid: base station MAC

## All tables

* The database schema follows ActiveRecord conventions: tables are plurals, 

foreign keys are singular_id, each table has an id primary key and 

created_at/updated_at timestamps.

* user_id is used to indicate which user recorded the interaction.

* Calls and messages tables have two timestamp columns. The 

call_timestamp/message_timestamp is the one recorded by the phone when the 

call/message was originally recorded. The timestamp column in the time at 

which the calldb/smsdb synchronisation occurred (which is less useful).

* Some tables have an "interaction" column. This was used for database 

synchronising and is left in for internal debugging purposes.

# Software and studies

Also included in the dataset download are programs for three sample studies. These are detailed below.

Each program can be run with ruby: for example, "ruby conversation-length.rb". The programs assume that your current working directory is the one with the database and the nodobo.rb code.

Software used:

* Ruby 1.8.7 or later, with gems: activerecord, sqlite3-ruby, progressbar

* gnuplot 4.4

* GraphViz 2.22

## Ruby interface: nodobo.rb

We have supplied a simple ActiveRecord interface to the database, "nodobo.rb". This gives classes and relations for each of the types of data in the dataset.

The interface can be used by running "irb -r ./nodobo.rb", or by using "require 'nodobo'" in your own programs. A sample irb session is given below:

>> u = User.find(19)

=> #

>> u.calls.size

=> 976

>> study_calls = u.calls.select {|c| c.other != nil }; study_calls.size

=> 133

>> Hash[study_calls.group_by(&:other_id).map {|k,v| [k, v.size]}]

=> {16=>2, 19=>1, 25=>2, 14=>4, 21=>124}

>> v = User.find(21)

=> #

>> v.calls.select {|c| c.other != nil }.size

=> 175

• sanitization: The following fields have been altered to remove personal information from the

dataset:                                                                       

  * Call#number, Message#number, User#number                                  

  * Device#mac, Presence#mac                                                 

  * Wifi#bssid                                                                 

  * Presence#name                                                              

  * Wifi#ssid                                                                  

  * CellTower#cellid                                                           

  * CellTower#lac                                                                                                         

Each real value for these fields maps 1:1 to a randomly-generated anonymous 

value. The process for generating these values is as follows:                                                                                 

  * Phone number: random number with the same number of digits; if original 

    number is 3 or more digits, keep the original first 2 digits                    

  * MAC address: 12 random hex digits                                             

  * Bluetooth name/Wifi ssid: random sequence of dictionary words, same number 

    of words as original name                                                  

  * Cell ID and LAC: random number with the same number of digits