Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Computer Engineering and Sciences

First Advisor

Carlos Enrique Otero

Second Advisor

Ivica Kostanic

Third Advisor

Susan Earles

Fourth Advisor

Muzaffar Shaikh


In order to tackle the problem of network connectivity and sensing coverage in random placement of sensor nodes over an area of interest, this research presents an optimization and visualization framework for characterization and prediction of optimal deployments of large-scale wireless sensor networks (WSNs). This study presents efficient image processing algorithms for classification of deployment terrain in order to optimize deployment and connectivity coverage with required minimum number of nodes and transmission power. It reports formation of efficient models for WSN signal propagation under different terrain conditions. The study describes the developmental approach of the WSN optimization framework and visualization techniques for large-scale deployments. Significant factors that determine the performance of WSN are included in the framework design. This study uses Poisson process and improved Fuzzy logic model to simulate and optimize deployment sensing coverage. Also, the research uses hard and soft computing techniques, disk communication model, and radio frequency (RF) propagation model to optimize sensing and connectivity coverage, and energy efficiency challenges. The performance of the framework is compared with actual small-scale stochastic deployment and existing simulator such as GloMoSim to demonstrate its optimal performance. Results show a significant difference and commonality. With the approach used in this study, deployment performance is improved by 20% - 40%. The results are also compared with theoretical, experimental and simulation deployment scenarios. Results show a feasible approach that can be used to automatically determine areas of high signal obstruction—which is essential to estimate obstruction parameters in simulations—and mapping of accurate WSN path loss models to enhance the overall optimization process during pre-deployment of large-scale WSN. The availability of the deployment framework and experimental data will improve the current and future applications of WSN technology such as in military operations, post disaster recovery response, environmental quality, surveillance, and border security.