Date of Award
12-1-2025
Degree Name
Master of Science
Department
Computer Science
First Advisor
Hexmoor, Henry
Second Advisor
Gupta, Bidyut
Third Advisor
Sinha, Koushik
Abstract
The rapid growth of the Internet of Things has led to the deployment of vast networks of interconnected devices, enabling real-time data collection and intelligent decision-making. However, traditional models for processing and responding to IoT data often face challenges in handling complex environments, large-scale operations, and dynamic user interactions. This thesis explores the integration of Large Action Models with IoT systems to enhance their efficiency, adaptability, and decision-making capabilities. Large Action Models, which leverage advanced machine learning techniques, offer the ability to process extensive datasets and execute complex sequences of actions with minimal human intervention. This work examines how LAMs can optimize IoT applications across key areas such as predictive maintenance, smart environments, and autonomous systems. Through empirical analysis and case studies, the research demonstrates that incorporating LAMs improves response time, system accuracy, and the ability to generalize to unseen conditions. The findings suggest that the synergy between IoT and LAMs represents a promising pathway for advancing intelligent automation and fostering more responsive and autonomous IoT ecosystems.
Access
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