Date of Award
3-2024
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Operational Sciences
First Advisor
Nicholas Boardman, PhD
Abstract
This research addresses the development of deployment policies for aerially dropped sensors in a wireless sensor network (WSN). Multi-objective genetic algorithm (GA) and simulated annealing meta-heuristic techniques, along with Monte Carlo simulation are used to identify policies with the aim of maximizing coverage and minimizing the number of sensors deployed. The policies developed from these techniques are then compared against uniform sensor distribution, as well as initial deployment policies that focus sensors in the center and edge of the region, as well as evenly deployed over the region. A total of 29 non-dominated policies were identified from the GA and SA techniques. Larger networks favored a modified even distribution, while smaller networks favored a modified center-heavy distribution. These non-dominated solutions range in size from 137 to 729 sensors and from 0.2389 to 0.9393 coverage. These solutions mostly meet or surpass coverage for initial solutions of comparable network sizes.
AFIT Designator
AFIT-ENS-MS-24-M-080
Recommended Citation
Fox, Noah E., "A Multi-Objective Approach to Optimal Deployment Policies for Wireless Sensor Networks Using Drop Points" (2024). Theses and Dissertations. 7715.
https://scholar.afit.edu/etd/7715
Comments
A 12-month embargo was observed for posting this work on AFIT Scholar.
Distribution Statement A, Approved for Public Release. PA case number on file.