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.

翻译：延迟容忍网络是一种专门为应对挑战性通信环境（如深空通信、灾难响应和偏远地区通信）而设计的网络架构与协议套件。尽管DTN[1]能够在高延迟、连接不稳定和高误码率的环境中提供高效可靠的数据传输，但其能耗优化问题仍是一个挑战，尤其在资源受限的场景中。为解决此问题，本研究将Epidemic[2]和MaxProp[3]路由协议与机器学习模型相结合，以优化DTN的能耗。在ONE模拟器中进行了数百次仿真，并导入了来自旧金山出租车移动轨迹[54]的外部真实数据集。采用随机森林[4]和梯度提升机（GBM）[5]模型进行数据分析。通过超参数调优和特征选择等优化手段，随机森林模型的R平方值达到0.53，而GBM模型的R平方值达到0.65。