Graph Theoretic Modeling and Energy Analysis of Wireless Telemetry Networks


Network science provides essential tools to model and analyze topology and structure of dynamic wireless telemetry networks. In this paper, we model wireless telemetry networks using three well-known graph models: Gilbert random graph, Erdős-Rényi random graph, and random geometric graph models. Next, we analyze the connectivity of synthetically generated topologies using graph energy, which is the sum of absolute values of eigenvalues. Our results indicate second-order curves for adjacency and Laplacian energies as the connectivity of synthetically generated networks improve. The normalized Laplacian energy decreases, converging to the theoretical lower bound as the connectivity reaches to a maximum.

Meeting Name

2017 International Telemetering Conference, ITC 2017 (2017: Oct. 23-26, Las Vegas, NV)


Electrical and Computer Engineering


We would like to acknowledge members of the Complex Networks and Systems (CoNetS) group for discussions on this work. Tristan A. Shatto is in part supported by Missouri University of Science and Technology (Missouri S&T) Opportunities for Undergraduate Research Experiences (OURE) Program. This work was also funded in part by the International Foundation for Telemetering (IFT).

Keywords and Phrases

Eigenvalues and eigenfunctions; Graph theory; Laplace transforms; Telemetering; Telemetering equipment; Absolute values; Energy analysis; Graph theoretic modeling; Network science; Normalized Laplacian; Random geometric graphs; Second orders; Wireless telemetry; Topology

International Standard Serial Number (ISSN)

0884-5123; 0074-9079

Document Type

Article - Conference proceedings

Document Version


File Type





© 2017 The Author(s), All rights reserved.

Publication Date

01 Oct 2017