DOI
https://doi.org/10.25772/ERJ2-5209
Defense Date
2014
Document Type
Thesis
Degree Name
Master of Science
Department
Engineering
First Advisor
Rosalyn Hargraves
Abstract
Biological networks (specifically genetic regulatory networks) are known to be robust to various external perturbations. Bio-inspired wireless sensor networks (WSN) are known to be smart communication structures and have a have high packet transmission efficiency. In earlier work neural network models that correlate the average packet receival rates to the five topological features of the bio-inspired WSN were investigated. These features include the degree index, sink coverage, network density, hub node density, and motif index. In this thesis, an appropriate classification algorithm that works with these five features is investigated. The random forest algorithm is the best classification algorithm compared to other classification methods (APPENDIX B). In addition, a local weighted linear regression algorithm was created to predict the robustness of the network utilizing these five topological features.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
8-18-2014