Temperature prediction using thermal fluctuations from wireless sensor networks in adaptive filter model
Name
1237419600-MIT.pdf
Size
12.18 MB
Format
Adobe PDF
Checksum (MD5)
6fa1682a0824d3e6c0282df8e532759f
Author(s)
Hwang, Mitchell D.
Advisor(s)
Joseph D. Steinmeyer.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
In many scientific experiments, it is imperative to minimize the unintended effects of variables other than the independent variables. Temperature, pressure, and gas levels are factors controlled to a certain extent using expensive climate-controlling technology, yet the resolution for monitoring their levels is generally low. The downward scaling of communication-enabled electronics in size, cost, and energy provides a potential toolset for tracking such data with high spatial and temporal resolutions. We establish a data collection methodology through a low-cost, small footprint distributed network system of modules that records data in a remote server. The system architecture allows for increased spatial resolutions, demonstrates high precision of measurements, and investigates room dynamics. Modules are fabricated using commercial sensors such as the ESP8266, BME680, and TCS34725. In this paper, we propose a temperature prediction model using adaptive filter methodologies to learn the relationship between thermal fluctuations at distinct locations within a lab environment.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 97-98).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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