Theses and Dissertations

Date of Award

5-2024

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Qi Lu

Second Advisor

Emmett Tomai

Third Advisor

Zhixiang Chen

Abstract

This thesis delves into the security of stochastic pheromone-based foraging algorithms within swarm robotic systems, a subset of foraging algorithms distinguished by their reliance on probabilistic decision-making mechanisms inspired by the natural world. Such algorithms face vulnerabilities in stigmergic communication that threaten to disrupt swarm operations. This research investigates these vulnerabilities, presenting two distinct contributions.

The first contribution examines the implementation of quarantine strategies as a defensive measure to isolate and mitigate the impact of fake resource attacks. By simulating these attacks, this study quantitatively assesses their detrimental effects on swarm efficiency and explores the efficacy of quarantine zones in preserving the integrity of swarm operations. The second contribution focuses on the application of a clustering analysis technique, specifically Density-Based Spatial Clustering of Applications with Noise (DBSCAN), to detect misleading pheromone trails and isolate the agents responsible for placing them. Through rigorous experimentation, this approach is shown to significantly improve the swarm's ability to maintain operational efficiency in the face of deceptive pheromone-based disruptions.

These contributions are pivotal in advancing the security and operational robustness of stochastic pheromone-based foraging robot swarms. Through comprehensive simulations, this thesis demonstrates the effectiveness of these strategies in countering adversarial threats, thereby contributing to the development of more secure and resilient swarm robotic systems. This work lays a foundation for future exploration into secure communication protocols for swarm robotics, with wide-ranging implications for environmental monitoring, search and rescue operations, and beyond.

Comments

Copyright 2024 Ryan A. Luna.

https://go.openathens.net/redirector/utrgv.edu?url=https://www.proquest.com/pqdtglobal1/dissertations-theses/developing-resilient-defense-strategies-against/docview/3085314799/sem-2?accountid=7119

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