用强化学习统一构建树结构与调度，降低物联网延迟。
Q-Learning-Based Time-Critical Data Aggregation Scheduling in IoT
- 将树构建与调度合并为马尔可夫决策过程，用Q-learning动态优化。
- 在300节点网络中，延迟比先进启发式算法低10.87%。
- 适合对时延敏感的智能城市、工业自动化等场景。
物联网中时延敏感的数据聚合需要高效、无冲突的调度以最小化延迟，适用于智慧城市和工业自动化等应用。传统启发式方法采用两阶段的树构建与调度，因静态特性常导致高计算开销和次优延迟。为此，本文提出一种新型Q-learning框架，将聚合树构建与调度统一建模为带哈希状态的马尔可夫决策过程（MDP），通过奖励函数引导大规模、无干扰的批量传输，动态学习最优调度策略。在最多300个节点的静态网络上进行仿真，结果表明，相比当前最先进的启发式算法，延迟降低最高达10.87%，展现出对时延敏感物联网应用的鲁棒性。该框架可实现在物联网环境中的及时数据洞察，为可扩展、低延迟的数据聚合提供新路径。
原文摘要 · Abstract (English)
Time-critical data aggregation in Internet of Things (IoT) networks demands efficient, collision-free scheduling to minimize latency for applications like smart cities and industrial automation. Traditional heuristic methods, with two-phase tree construction and scheduling, often suffer from high computational overhead and suboptimal delays due to their static nature. To address this, we propose a novel Q-learning framework that unifies aggregation tree construction and scheduling, modeling the process as a Markov Decision Process (MDP) with hashed states for scalability. By leveraging a reward function that promotes large, interference-free batch transmissions, our approach dynamically learns optimal scheduling policies. Simulations on static networks with up to 300 nodes demonstrate up to 10.87% lower latency compared to a state-of-the-art heuristic algorithm, highlighting its robustness for delay-sensitive IoT applications. This framework enables timely insights in IoT environments, paving the way for scalable, low-latency data aggregation.
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