Date of Award

Spring 5-4-2026

Access Type

Thesis - Open Access

Degree Name

Master of Systems Engineering

Department

Electrical, Computer, Software, and Systems Engineering

Committee Chair

Bryan C. Watson

Committee Chair Email

watsonb3@erau.edu

Committee Advisor

Bryan C. Watson

Committee Advisor Email

watsonb3@erau.edu

First Committee Member

Heidi M. Steinhauer

First Committee Member Email

steinhah@erau.edu

Second Committee Member

Daniel G. Penny III

Second Committee Member Email

pennyd@erau.edu

College Dean

James W. Gregory

Abstract

Effective communication is a critical component of successful collaboration in group projects and team settings. However, systematically tracking and analyzing team communications can be challenging, especially in complex, multi-member teams such as those found in the aerospace industry. This paper explores an information-theoretic approach that leverages encoding techniques and graph theory to analyze communication networks within a university’s multi-year, student-led cubesat design project (Project COMET). By utilizing Shannon Entropy as a measure of information flow, encoding communication patterns, and analyzing Ecological Network Analysis parameters, this research aims to understand how an Embry-Riddle Aeronautical University student project team evolves over the first 52 weeks of the project. This thesis answers two primary questions and two secondary questions: (1) How can we quantitatively describe an aerospace team communication structure and associated network characteristics using ENA, Graph Theory, and Information Theory? (2) How does Project COMET evolve over a specified period of time? (3) What are the structural indicators in Project COMET that are consistent with a mature ecosystem vs. immature ecosystem? (4) How can the results of this paper influence the development of new and improved software technology to help managers better understand team networks? Graph theory enabled the identification of communication structures, key actors or nodes, information bottlenecks, and interaction patterns. Results indicate that Ecological Network Analysis metrics, including Number of Actors, Number of Links, Linkage Density, Betweenness, Eigenvector Centrality, and Total System Overhead (normalized) were consistent with other research and ecosystem literature, in that they presented values corresponding with a maturing ecosystem. This work provides a key first step towards a novel, in-situ approach for quantifying member interactions, identifying communication dynamics, and detecting potential communication breakdowns and bottlenecks. By linking ecological network parameters with team communication data, this paper seeks to help quantify and provide insights into key performance indicators like team dynamics, collaboration efficiency, and adaptive strategies for observing information flow in team structures. In the long term, this research aims to lay the foundation for the advancement of structured communication tracking and improved teamwork through use of educational and professional insight tools.

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