2026-09-19T06:27:41Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/325092022-01-13T07:55:02Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
V. Michael Bove, Jr.
Pilpré, Arnaud
Massachusetts Institute of Technology. Dept. of Architecture. Program In Media Arts and Sciences
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
2006-03-29T18:51:12Z
2006-03-29T18:51:12Z
2005
2005
http://hdl.handle.net/1721.1/32509
61923399
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).
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.
by Arnaud Pilpré.
S.M.
87 p.
4472779 bytes
4476952 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Architecture. Program In Media Arts and Sciences
Self-* properties of multi sensing entities in smart environments
Thesis
application/pdf
Publication
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unknown
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<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>
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