CRAWDAD uw/places

Citation Author(s):
Jong Hee
Kang
University of Washington
Gaetano
Borriello
William
Welbourne
University of Washington
Benjamin
Stewart
University of Washington
Submitted by:
CRAWDAD Team
Last updated:
Tue, 11/14/2006 - 08:00
DOI:
10.15783/C72C7Q
Data Format:
License:
34 Views
Categories:
Keywords:
0
0 ratings - Please login to submit your rating.

Abstract 

Location-aware dataset for extracting significant places.

Real, long-term data collected from three participants using a Place Lab client, from which the authors extract significant places.

date/time of measurement start: 2004-06-07 

date/time of measurement end: 2004-06-10 

collection environment: Location-aware systems are proliferating on a variety of platforms from laptops to cell phones. Though these systems offer two principal representations in which to work with location (coordinates and landmarks) they do not offer a means for working with the userlevel notion of "place". A place is a locale that is important to a user and which carries a particular semantic meaning such as "my place of work", "the place we live" or "my favorite lunch spot". The authors propose an algorithm for extracting significant places from a trace of coordinates. The authors experimentally evaluate the algorithm with real, long-term data collected from three participants using a Place Lab client, a software client that computes location coordinates by listening for RF-emissions from known radio beacons in the environment (e.g. 802.11 access points, GSM cell towers). 

network configuration: As all the trace collectors typically stay within the Seattle city limits, and as most of this area is covered by the Place Lab AP database, there were few problems with location data being unavailable. 

data collection methodology: The authors use Place Lab to collect traces of location coordinates. Place Lab provides a way for a WiFi-enabled client device to automatically determine its location by listening to RF-emissions from known 802.11 access points (APs) in the environment. Specifically, the system exploits the fact that each AP periodically broadcasts its unique MAC address as part of its management beacon. A client holds a database of (MAC address, latitude and longitude) pairs which it uses to compute its location from heard beacons. When the client device receives beacon messages from nearby APs, it retrieves each AP's location from the database and computes its own location as the average of retrieved coordinates, using a simple centroid tracking scheme. 

Traceset

uw/places/placelab

Packet captures of BitTorrent traffic from Korea Telecom's mobile WiMAX network in Seoul, with payload cut off and with headers anonymized.

  • files: campus.tar.gz, city-wide.tar.gz
  • measurement purpose: Location-aware Computing
  • methodology: Location coordinates were generated and logged at a rate of once per second using Place Lab's centroid tracker. For initial evaluation, two day-length traces were collected during the daily routines of the first and second authors. The traces were collected with wireless mobile devices (e.g. laptop, PDA), and corresponding place logs were also kept.
  • sanitization: The payload was cut off by resampling with tcpreplay and tcpdump, and the header was anonymized by tcprewrite.

uw/places/placelab Traces

  • campus: Two-hour long Place Lab trace collected on the campus of University of Washington, Seattle.
    • configuration: The first trace segment, over a small area, is of an author'sdaily errands around the university campus and lasts for about 2 hours. The trace log was started in the author's office. After about 10 minutes in his office, the author left to go home. On his way off campus, the author ran errands in five buildings across campus (places 2 through 6), staying 9 to 20 minutes in each place.
    • format: The traces are written in xml format. So, it is self-describing and easy to understand. For example, in the campus trace, we measured the AP signals every second. And, for each measurement, our logging program appends a element to the trace. The element includes timestamp, coordinate computed with placelab using centroid tracker, and the list of detected access points.
  • campus: 12-hour long Place Lab trace collected city-wide in Seattle, WA.
    • configuration: The second trace is of an author's daily movementbetween home, work, lunch, school, and a friend'shouse with a total duration of about 12 hours. The trace starts at the author's home in the morning. After about 30 minutes, he headed to his place of work. At work, he attended a meeting in a conference room in one corner of the building, and spent the rest of the time at his desk in the other corner. After a few hours, he left to attend two meetings in another building on campus - each meeting was held in a different room. After the second meeting ended, he returned to his pace of work. At lunch time, he went out to eat at a restaurant a few blocks away. At the endof the day, he visited a shopping mall and hisfriend's house before returning home.
    • format: The traces are written in xml format. So, it is self-describing and easy to understand. For example, in the campus trace, we measured the AP signals every second. And, for each measurement, our logging program appends a element to the trace. The element includes timestamp, coordinate computed with placelab using centroid tracker, and the list of detected access points.

 

Instructions: 

The files in this directory are a CRAWDAD dataset hosted by IEEE DataPort. 

About CRAWDAD: the Community Resource for Archiving Wireless Data At Dartmouth is a data resource for the research community interested in wireless networks and mobile computing. 

CRAWDAD was founded at Dartmouth College in 2004, led by Tristan Henderson, David Kotz, and Chris McDonald. CRAWDAD datasets are hosted by IEEE DataPort as of November 2022. 

Note: Please use the Data in an ethical and responsible way with the aim of doing no harm to any person or entity for the benefit of society at large. Please respect the privacy of any human subjects whose wireless-network activity is captured by the Data and comply with all applicable laws, including without limitation such applicable laws pertaining to the protection of personal information, security of data, and data breaches. Please do not apply, adapt or develop algorithms for the extraction of the true identity of users and other information of a personal nature, which might constitute personally identifiable information or protected health information under any such applicable laws. Do not publish or otherwise disclose to any other person or entity any information that constitutes personally identifiable information or protected health information under any such applicable laws derived from the Data through manual or automated techniques. 

Please acknowledge the source of the Data in any publications or presentations reporting use of this Data. 

Citation: Jong Hee Kang, Gaetano Borriello, William Welbourne, Benjamin Stewart, uw/places, https://doi.org/10.15783/C72C7Q , Date: 20060502

Dataset Files

LOGIN TO ACCESS DATASET FILES
Open Access dataset files are accessible to all logged in  users. Don't have a login?  Create a free IEEE account.  IEEE Membership is not required.

Documentation

AttachmentSize
File uw-places-readme.txt1.61 KB

These datasets are part of Community Resource for Archiving Wireless Data (CRAWDAD). CRAWDAD began in 2004 at Dartmouth College as a place to share wireless network data with the research community. Its purpose was to enable access to data from real networks and real mobile users at a time when collecting such data was challenging and expensive. The archive has continued to grow since its inception, and starting in summer 2022 is being housed on IEEE DataPort.

Questions about CRAWDAD? See our CRAWDAD FAQ. Interested in submitting your dataset to the CRAWDAD collection? Get started, by submitting an Open Access Dataset.