Chunxin Wang , Wenyu Qu , Rui Hou , Feng Jiao , Ying Zou
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The method integrates network temporal features extracted by the Transformer algorithm with rule-based features from Google's Congestion Control (GCC) algorithm, and employs Proximal Policy Optimization (PPO) reinforcement learning to generate real-time transmission rate control strategies. This approach improves both the efficiency and robustness of deep reinforcement learning algorithms for congestion control. The performance evaluation metrics used for comparison are Quality of Service (QoS), mainly focusing on throughput, latency, and average packet loss rate. Compared to other algorithms, this model achieves the best performance with a score of 88.13. 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引用次数: 0
摘要
物联网(IoT)与智能电网的融合越来越强调实现高传输稳定性。随着异构设备大量集成到电力物联网中,传输数据量激增,网络动态复杂性增加,传输延迟导致数据拥塞时间延长。现有的拥塞控制算法通常分为基于规则的方法和基于学习的方法。然而,完全依赖于单一类型的算法可能会导致诸如次优带宽利用率或缓慢收敛等挑战。为了提高数据传输的稳定性,本文提出了一种将基于规则的模型与数据驱动模型相结合的拥塞控制方法。该方法将Transformer算法提取的网络时间特征与b谷歌的拥塞控制(GCC)算法基于规则的特征相结合,采用PPO (Proximal Policy Optimization)强化学习生成实时传输速率控制策略。这种方法提高了用于拥塞控制的深度强化学习算法的效率和鲁棒性。用于比较的性能评估指标是QoS (Quality of Service),主要关注吞吐量、延迟和平均丢包率。与其他算法相比,该模型的性能最好,得分为88.13分。这种混合算法为未来的网络拥塞控制提供了一个很好的方向。
Fusion-based congestion control method for the power internet of things combining data-driven and rule-engine models
The integration of the Internet of Things (IoT) into smart grids is placing an ever-growing emphasis on achieving high transmission stability. With the massive integration of heterogeneous devices into the power IoT, the volume of transmitted data has surged, and the dynamic complexity of networks has increased, leading to prolonged data congestion due to delayed transmission. Existing congestion control algorithms are typically divided into rule-based and learning-based approaches. However, relying exclusively on a single type of algorithm can result in challenges like suboptimal bandwidth utilization or slow convergence. To enhance data transmission stability, this paper introduces a congestion control method that integrates rule-based models with data-driven models. The method integrates network temporal features extracted by the Transformer algorithm with rule-based features from Google's Congestion Control (GCC) algorithm, and employs Proximal Policy Optimization (PPO) reinforcement learning to generate real-time transmission rate control strategies. This approach improves both the efficiency and robustness of deep reinforcement learning algorithms for congestion control. The performance evaluation metrics used for comparison are Quality of Service (QoS), mainly focusing on throughput, latency, and average packet loss rate. Compared to other algorithms, this model achieves the best performance with a score of 88.13. The hybrid algorithm offers a promising direction for future network congestion control.
期刊介绍:
Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT.
The journal will place a high priority on timely publication, and provide a home for high quality.
Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.