Noise Modeling Using Internet of Things
Loading...
Date
2017
Authors
Deodhar, Anupama Vijay
Journal Title
Journal ISSN
Volume Title
Publisher
University of Oregon
Abstract
Data collected through Internet of Things (IoT) technology has begun to revolutionize the utilization of buildings. Having a wealth of information about noise, lighting, temperature and more collected through accessible, low-cost means allows buildings to be readily customized to increase efficiency and reduce energy costs. The purpose of this project is to prove the feasibility of creating a predictive noise model using low-cost, low-power sensor hardware. Previous research has not adequately addressed how IoT methodologies can be implemented to create noise models, but rather focused on other tools and methods. Furthermore, related research largely takes place outside of the United States suggesting a void in both collected data and research surrounding noise and its applications in America. Noise data was collected in the Knight Library using microphone sensors and the Intel Edison, an IoT device. Results were visualized through a web application, which highlighted relationships between location, time, and noise levels. The resulting models indicated an ability to predict trends over time Within a university scope, students can use the resulting models to locate quiet study locations. Outside of a student-oriented scope, having access to noise models in a visual and easily-digestible way provides valuable feedback to inform future building design, improve campus efficiency, and spark discussion about hosting smart buildings on campus.
Description
101 pages. A thesis presented to the Department of Computer & Information Science and the Clark Honors College of the University of Oregon in partial fulfillment of the requirements for degree of Bachelor of Science, Spring 2017
Keywords
Internet of Things (IOT), Noise modeling, Predictive modeling, Computer science, Smart environments, Building efficiency