Data From: The Distributed Trust Model Applied to the Energy Grid of Things

Authors

Narmada Sonali Fernando, Portland State University

Sponsor

Portland State University. Department of Electrical and Computer Engineering

Document Type

Dataset

Publication Date

11-2021

Abstract

These files support research on the Distributed Trust Model’s (DTM) Hypothesis Tool analyzes Metric Vector of Trust (MVoT) data. The DTM Hypothesis Tool is developed using Microsoft® Excel® for Microsoft 365 (64-bit). This tool helps set correct thresholds to send alerts at the appropriate times, helps evaluate the equations used for sending those alerts, and helps assess MVoT equations. In addition, the DTM Hypothesis Tool helps analyze the effect of the error rate for a selected threshold that determines when to send alerts.

This tool allows users to adjust and choose the preferred threshold value with their liking of error rate probability. In addition, this tool enables the user to understand how effective the decision-making equations consist of thresholds. It also helps the user determine the initial set of MVoT equations is appropriate or needs changing. The dataset supports a Master of Science (M.S.) in Electrical and Computer Engineering thesis, the Distributed Trust Model Applied to the Energy Grid of Things.

The dataset supports a Master of Science (M.S.) in Electrical and Computer Engineering thesis, The Distributed Trust Model Applied to the Energy Grid of Things.

Thesis Advisor: John M. Acken, Ph.D

Description

The DTM Hypothesis Tool operates within Microsoft® Excel® for Microsoft 365 (64-bit). The archived files consist of Hypothesis Tool Data and input PerTPlot files, all in the form of Comma Separated Values (CSV) files. There are 208 hypothesis analysis .xlsx files and 208 .csv data files.

Rights

This work is marked with CC0 1.0 Universal

DOI

10.15760/ece-data.04

Persistent Identifier

https://archives.pdx.edu/ds/psu/36685

Recommended Citation

Fernando, N. S., "Data From: The Distributed Trust Model Applied to the Energy Grid of Things" (2021). Electrical and Computer Engineering Datasets. 4. https://doi.org/10.15760/ece-data.04