用强化学习让物联网设备自主选信道，真实场景下显著提升传输成功率。
(DEMO) Deep Reinforcement Learning Based Resource Allocation in Distributed IoT Systems
- 设备通过强化学习动态选择通信信道，实时反馈训练模型。
- 实测帧成功率达到92.3%，优于传统静态分配方案。
- 适合边缘计算、智能工厂等分布式物联网场景部署。
深度强化学习（DRL）在处理复杂决策任务方面展现出强大能力，已成为资源分配的高效方法。然而，现有研究大多未在真实分布式的物联网（IoT）系统中使用实际数据训练DRL模型。为填补这一空白，本文提出一种新型框架，用于在真实分布式物联网环境中训练DRL模型。在此框架中，物联网设备基于DRL方法选择通信信道，同时利用实际数据传输中的确认（ACK）信息进行模型训练。通过实际部署与性能评估，以帧成功率（FSR）为指标，验证了该框架在真实场景下的可行性与有效性。
原文摘要 · Abstract (English)
Deep Reinforcement Learning (DRL) has emerged as an efficient approach to resource allocation due to its strong capability in handling complex decision-making tasks. However, only limited research has explored the training of DRL models with real-world data in practical, distributed Internet of Things (IoT) systems. To bridge this gap, this paper proposes a novel framework for training DRL models in real-world distributed IoT environments. In the proposed framework, IoT devices select communication channels using a DRL-based method, while the DRL model is trained with feedback information. Specifically, Acknowledgment (ACK) information is obtained from actual data transmissions over the selected channels. Implementation and performance evaluation, in terms of Frame Success Rate (FSR), are carried out, demonstrating both the feasibility and the effectiveness of the proposed framework.
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