用机器学习预测TSCH网络空闲时段，让节点深度休眠省电。
Machine Learning to Predict Slot Usage in TSCH Wireless Sensor Networks
- 基于机器学习分析TSCH网络流量模式，预判空闲期。
- 在树状拓扑中，靠近根节点的预测精度下降。
- 模拟实验表明可显著降低TSCH网络功耗。
无线传感器网络（WSNs）广泛应用于工业场景，需满足超低功耗与确定性通信的双重要求。时间槽通道跳频（TSCH）技术恰好兼顾这两点，成为工业应用的理想选择。本文提出利用机器学习模型学习基于TSCH协议生成的流量模式，使节点在无数据传输计划时进入深度睡眠状态，从而提升网络能效。研究深入分析了机器学习模型在典型树形拓扑下不同网络层级的预测能力，发现其性能随靠近树根而下降。基于对无线传感器节点精确建模的仿真数据验证表明，所研究算法可有效且显著降低TSCH网络的功耗。
原文摘要 · Abstract (English)
Wireless sensor networks (WSNs) are employed across a wide range of industrial applications where ultra-low power consumption is a critical prerequisite. At the same time, these systems must maintain a certain level of determinism to ensure reliable and predictable operation. In this view, time slotted channel hopping (TSCH) is a communication technology that meets both conditions, making it an attractive option for its usage in industrial WSNs. This work proposes the use of machine learning to learn the traffic pattern generated in networks based on the TSCH protocol, in order to turn nodes into a deep sleep state when no transmission is planned and thus to improve the energy efficiency of the WSN. The ability of machine learning models to make good predictions at different network levels in a typical tree network topology was analyzed in depth, showing how their capabilities degrade while approaching the root of the tree. The application of these models on simulated data based on an accurate modeling of wireless sensor nodes indicates that the investigated algorithms can be suitably used to further and substantially reduce the power consumption of a TSCH network.
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