Sensor networks for social networks

This thesis outlines the development of software that makes use of Bayesian belief networks and signal processing techniques to make meaningful inferences about real-world phenomena using data obtained from sensor networks. The effectiveness of the software is validated by applying it to the problem of detecting face-to-face social interactions between groups of people, given data readings from sensors that record light, temperature, acceleration, sound, and proximity. This application represents a novel method for social network construction which is potentially more accurate and less intrusive than traditional methods, but also more meaningful than newer methods that analyze digitally mediated communication.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2006

Author dc:creator

• Farry, Michael P. (Michael Patrick)

Advisor dc:contributor.advisor

• Samuel Madden.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/36764

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/36764