Graph Theoretic Modeling and Energy Analysis of Wireless Telemetry Networks

Abstract

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, Erdos-Renyi 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.