Publication Date
2025
Document Type
Thesis
Committee Members
Bin Wang, Ph.D. (Advisor); Meilin Liu, Ph.D. (Committee Member); Krishnaprasad Thirunarayan, Ph.D. (Committee Member)
Degree Name
Master of Science (MS)
Abstract
Ad Hoc wireless networks, with their decentralized architecture and dynamic topology, present challenges in reliable and energy-efficient routing. While machine learning (ML) and reinforcement learning (RL) offer promising solutions, progress is limited by the lack of realistic, high-fidelity datasets. This research introduces a simulation-based framework for generating four diverse datasets representing combinations of node mobility (mobile vs. static) and spatial distribution (random vs. clustered). Each dataset captures critical metrics such as Signal-to-Interference-plus-Noise Ratio (SINR), bottleneck rate, and power consumption across multi-hop paths. A lookahead-based greedy routing algorithm with scenario-aware power control is implemented to emulate practical behavior. Supervised ML models, particularly Random Forest classifiers, are trained on the datasets to assess Quality of Transmission (QoT), achieving over 97% accuracy across all scenarios. The framework enables performance benchmarking and supports intelligent routing policy development, addressing a key gap in ML-based Ad Hoc networking research
Page Count
56
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2025
Copyright
Copyright 2025, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
ORCID ID
0009-0000-8598-8684
