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Joseph D. Steinmeyer. Hwang, Mitchell D. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2021-02-19T20:48:34Z 2021-02-19T20:48:34Z 2020 2020 https://hdl.handle.net/1721.1/129903 1237419600 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). 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. by .Mitchell D. Hwang M.Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 98 pages eng Massachusetts Institute of Technology 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. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Temperature prediction using thermal fluctuations from wireless sensor networks in adaptive filter model Thesis application/pdf 2021-02-19T20:48:04Z Publication Master EECS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="15f44dd3-211a-429c-a7c1-7cc3dc1e9c53"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Temperature prediction using thermal fluctuations from wireless sensor networks in adaptive filter model</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2020</PublicationDate> <Authors> <Author> <DisplayName>Hwang, Mitchell D.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>