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arXiv 2406.03580cs.NI
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延迟容忍网络中的能耗优化

Optimization of Energy Consumption in Delay-Tolerant Networks

Junran Wang, Milena Radenkovic

AI总结:

本文针对延迟容忍网络在资源受限场景下的能耗优化问题,结合Epidemic和MaxProp路由协议与机器学习模型,在ONE模拟器和旧金山出租车轨迹数据集上进行仿真分析,通过超参数调优和特征选择,使GBM模型取得0.65的R平方值,优于随机森林的0.53。

摘要:

延迟容忍网络(DTN)是一种专门为应对深空通信、灾害响应和偏远地区通信等具有挑战性的通信环境而设计的网络架构和协议套件。尽管DTN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然是一个挑战,尤其是在资源有限的场景下。为了解决这一问题,本研究将Epidemic和MaxProp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的外部真实世界数据集。采用随机森林(Random Forest)和梯度提升机(GBM)模型进行数据分析。通过涉及超参数调优和特征选择的优化,随机森林模型取得了0.53的R平方值,而GBM模型取得了0.65的R平方值。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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