{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/32509"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/32509","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Self-* properties of multi sensing entities in smart environments","abstract":"Computers and sensors are more and more often embedded into everyday objects, woven into garments, \"painted\" on architecture or deployed directly into the environment. They monitor the environment, process the information and extract knowledge that their designed and programmers hope will be interesting. As the number and variety of these sensors and their connections increase, so does the complexity of the networks in which they operate. Deployment, management, and repair become difficult to perform manually. It is, then, particularly appealing to design a software architecture that can achieve the necessary organizational structures without requiring human intervention. Focusing on image sensing and machine vision techniques, we propose to investigate how small, unspecialized, low-processing sensing entities can self-organize to create a scalable, fault tolerant, decentralized, and easily reconfigurable system for smart environments and how these entities self-adapt to optimize their contribution in the presence of constraints inherent to sensor networks.","abstract_html":"Computers and sensors are more and more often embedded into everyday objects, woven into garments, "painted" on architecture or deployed directly into the environment. They monitor the environment, process the information and extract knowledge that their designed and programmers hope will be interesting. As the number and variety of these sensors and their connections increase, so does the complexity of the networks in which they operate. Deployment, management, and repair become difficult to perform manually. It is, then, particularly appealing to design a software architecture that can achieve the necessary organizational structures without requiring human intervention. Focusing on image sensing and machine vision techniques, we propose to investigate how small, unspecialized, low-processing sensing entities can self-organize to create a scalable, fault tolerant, decentralized, and easily reconfigurable system for smart environments and how these entities self-adapt to optimize their contribution in the presence of constraints inherent to sensor networks.","abstract_has_math":false,"creators":["Pilpré, Arnaud"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Architecture. Program In Media Arts and Sciences","school":null,"contributors":[],"advisors":["V. Michael Bove, Jr."],"committee_chairs":[],"committee_members":[],"year":2005,"date_issued":"2005","date_published":"2005","updated_at":"2026-07-22T22:20:58Z","subjects":["Architecture. Program In Media Arts and Sciences"],"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/1721.1/32509","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["V. Michael Bove, Jr."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Dept. of Architecture. Program In Media Arts and Sciences"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Dept. of Architecture. 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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/1721.1/32509"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.","Includes bibliographical references (p. 78-87)."]},{"key":"dc:description.abstract","label":"Abstract","values":["Computers and sensors are more and more often embedded into everyday objects, woven into garments, \"painted\" on architecture or deployed directly into the environment. They monitor the environment, process the information and extract knowledge that their designed and programmers hope will be interesting. As the number and variety of these sensors and their connections increase, so does the complexity of the networks in which they operate. Deployment, management, and repair become difficult to perform manually. It is, then, particularly appealing to design a software architecture that can achieve the necessary organizational structures without requiring human intervention. Focusing on image sensing and machine vision techniques, we propose to investigate how small, unspecialized, low-processing sensing entities can self-organize to create a scalable, fault tolerant, decentralized, and easily reconfigurable system for smart environments and how these entities self-adapt to optimize their contribution in the presence of constraints inherent to sensor networks."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Self-* properties of multi sensing entities in smart environments"]}]}],"canonical_facts":{"dc:contributor.advisor":["V. Michael Bove, Jr."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Architecture. Program In Media Arts and Sciences"],"dc:contributor.other":["Massachusetts Institute of Technology. Dept. of Architecture. Program In Media Arts and Sciences"],"dc:creator":["Pilpré, Arnaud"],"dc:date.accessioned":["2006-03-29T18:51:12Z"],"dc:date.available":["2006-03-29T18:51:12Z"],"dc:date.issued":["2005"],"dc:description":["Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.","Includes bibliographical references (p. 78-87)."],"dc:description.abstract":["Computers and sensors are more and more often embedded into everyday objects, woven into garments, \"painted\" on architecture or deployed directly into the environment. They monitor the environment, process the information and extract knowledge that their designed and programmers hope will be interesting. As the number and variety of these sensors and their connections increase, so does the complexity of the networks in which they operate. Deployment, management, and repair become difficult to perform manually. It is, then, particularly appealing to design a software architecture that can achieve the necessary organizational structures without requiring human intervention. Focusing on image sensing and machine vision techniques, we propose to investigate how small, unspecialized, low-processing sensing entities can self-organize to create a scalable, fault tolerant, decentralized, and easily reconfigurable system for smart environments and how these entities self-adapt to optimize their contribution in the presence of constraints inherent to sensor networks."],"dc:description.degree":["S.M."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["http://hdl.handle.net/1721.1/32509"],"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":["Architecture. Program In Media Arts and Sciences"],"dc:title":["Self-* properties of multi sensing entities in smart environments"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:20:58Z"}