  Delay tolerant network is a network architecture and protocol suite
specifically designed to handle challenging communications environments, such
as deep space communications, disaster response, and remote area
communications. Although DTN [1]can provide efficient and reliable data
transmission in environments with high latency, unstable connections, and high
bit error rates, its energy consumption optimization problem is still a
challenge, especially in scenarios with limited resources.To solve this
problem, this study combines the Epidemic[2] and MaxProp[3] routing protocols
with Machine Learning Models to optimize the energy consumption of DTNs.
Hundreds of simulations were conducted in the ONE simulator, and an external
real-world dataset from San Francisco taxi mobility traces [54] was imported.
Random Forest[4] and Gradient Boosting Machine (GBM)[5] models were employed
for data analysis. Through optimization involving Hyperparameter Tuning and
Feature Selection, the Random Forest model achieved an R-squared value of 0.53,
while the GBM model achieved an R-squared value of 0.65.
