Abstract

Instrumentation for collecting geophysical data, specifically heat flow, in lake and marine environments has been in existence for over fifty years. Despite this, the costs associated with data collection and the technological limitations of existing instrumentation can be preventative when conducting geophysical studies. Furthermore, the success rate of such studies is limited by the lack of real time data transmission capabilities when instruments are deployed for extended periods of time. As a solution to this problem we have created cost-effective and lightweight IoT-driven instrumentation and combined it with cloud computing technology facilitating real time data transmission to the cloud. Furthermore, we have also explored a new data analysis technique employing Granger causality-based time series clustering to investigate relationships between atmospheric data and water column temperature data.

Degree Date

Winter 12-18-2021

Document Type

Thesis

Degree Name

M.S.

Department

Computer Science and Engineering

Advisor

Eric Larson

Subject Area

Computer Science

Number of Pages

68

Format

.pdf

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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