Publication Date
Spring 2024
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Navrati Saxena
Second Advisor
Katerina Potika
Third Advisor
Abhishek Roy
Keywords
Routing, Long Short Term Memory, Satellite Networks, Machine Learning
Abstract
Satellite networks are one of the most important components that fulfill the world’s need for connectivity. To ensure that communication is efficient and reliable, robust routing algorithms are a must. Because, although it is true that certain routing characteristics may not be permanently and continuously flawless, a routing technique must effectively adapt to modifications in such network characteristics. The new routing method uses a Long Short-Term Memory (LSTM) model to manage dynamic metrics for Low Earth Orbit satellite networks. This LSTM model is aimed at predicting the optimal routing direction on the premise that a satellite is soon to be, by looking back through old satellite position data. Through simulation and analysis, the upgraded algorithm with LSTM prediction brought large profit in network efficiency, which includes lowering end-to-end delay (reduction of average packet life time) and raising throughput. These results underscore just how important is this research into satellite networks.
Recommended Citation
Bhamare, Yash, "Optimization of Inter-Satellite Routing using LSTM-based Path Prediction Model" (2024). Master's Projects. 1378.
https://scholarworks.sjsu.edu/etd_projects/1378
