2026-09-18T23:40:14Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/331122026-06-06T01:04:24Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Joseph Paradiso.
Broxton, Michael Joseph
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2006-06-19T17:41:44Z
2006-06-19T17:41:44Z
2005
2005
http://hdl.handle.net/1721.1/33112
62232852
Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
Includes bibliographical references (p. 117-124).
The utility and purpose of a node in a wireless sensor network is intimately tied to the physical space in which it is distributed. As such, it is advantageous under most circumstances for a sensor node to know its position. In this work, we present two systems for localizing a network of roughly 60 sensor nodes distributed over an area of 1-m2. One is based on a linear lateration technique, while the second approach utilizes non-linear optimization techniques, namely spectral graph drawing and mesh relaxation. In both cases, localization is accomplished by generating distance constraints based on ultrasound time-of-flight measurements to distinct, global sensor stimuli. These distance constraints alone are sufficient to achieve localization; no a priori knowledge of sensor node coordinates or the coordinates of the global sensor events are required. Using this technique, we have achieved a localization error of 2.30-cm and an error standard deviation of 2.36-cm.
by Michael Joseph Broxton.
M.Eng.
S.B.
124 p.
7439785 bytes
7446044 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Localization and sensing applications in the Pushpin Computer Network
Thesis
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<Abstract>The utility and purpose of a node in a wireless sensor network is intimately tied to the physical space in which it is distributed. As such, it is advantageous under most circumstances for a sensor node to know its position. In this work, we present two systems for localizing a network of roughly 60 sensor nodes distributed over an area of 1-m2. One is based on a linear lateration technique, while the second approach utilizes non-linear optimization techniques, namely spectral graph drawing and mesh relaxation. In both cases, localization is accomplished by generating distance constraints based on ultrasound time-of-flight measurements to distinct, global sensor stimuli. These distance constraints alone are sufficient to achieve localization; no a priori knowledge of sensor node coordinates or the coordinates of the global sensor events are required. Using this technique, we have achieved a localization error of 2.30-cm and an error standard deviation of 2.36-cm.</Abstract>
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