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

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