{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/36764"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/36764","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Sensor networks for social networks","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Farry, Michael P. (Michael Patrick)"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Samuel Madden."],"committee_chairs":[],"committee_members":[],"year":2006,"date_issued":"2006","date_published":"2006","updated_at":"2026-07-22T22:22:16Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/36764","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Samuel Madden."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."]},{"key":"dc:creator","label":"Author","values":["Farry, Michael P. (Michael Patrick)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2007-03-12T17:51:36Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2007-03-12T17:51:36Z"]},{"key":"dc:date.issued","label":"Date","values":["2006"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical Engineering and Computer Science."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["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."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/36764"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.","Includes bibliographical references (p. 51-55)."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Sensor networks for social networks"]}]}],"canonical_facts":{"dc:contributor.advisor":["Samuel Madden."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:creator":["Farry, Michael P. (Michael Patrick)"],"dc:date.accessioned":["2007-03-12T17:51:36Z"],"dc:date.available":["2007-03-12T17:51:36Z"],"dc:date.issued":["2006"],"dc:description":["Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.","Includes bibliographical references (p. 51-55)."],"dc:description.abstract":["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."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/36764"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Sensor networks for social networks"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:22:16Z"}