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Rapid and Resilient LoRa Leap: A Novel Multi-Hop Architecture for Decentralised Earthquake Early Warning Systems

机译:快速且有弹性的 LoRa Leap:一种用于分散式地震预警系统的新型多跳架构

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摘要

We introduce a novel LoRa-based multi-hop communication architecture as an alternative to the public internet for earthquake early warning (EEW). We examine its effectiveness in generating a meaningful warning window for the New Zealand-based decentralised EEW sensor network implemented by the CRISiSLab operating with the adapted Propagation of Local Undamped Motion (PLUM)-based earthquake detection and node-level data processing. LoRa, popular for low-power, long-range applications, has the disadvantage of long transmission time for time-critical tasks like EEW. Our network overcomes this limitation by broadcasting EEWs via multiple short hops with a low spreading factor (SF). The network includes end nodes that generate warnings and relay nodes that broadcast them. Benchmarking with simulations against CRISiSLab’s EEW system performance with internet connectivity shows that an SF of 8 can disseminate warnings across all the sensors in a 30 km urban area within 2.4 s. This approach is also resilient, with the availability of multiple routes for a message to travel. Our LoRa-based system achieves a 1–6 s warning window, slightly behind the 1.5–6.75 s of the internet-based performance of CRISiSLab’s system. Nevertheless, our novel network is effective for timely mental preparation, simple protective actions, and automation. Experiments with Lilygo LoRa32 prototype devices are presented as a practical demonstration.
机译:我们引入了一种新颖的基于 LoRa 的多跳通信架构,作为地震预警 (EEW) 公共互联网的替代方案。我们研究了它在为新西兰的分散式 EEW 传感器网络生成有意义的警告窗口方面的有效性,该网络由 CRISiSLab 实施,并使用基于局部无阻尼运动传播 (PLUM) 的自适应地震检测和节点级数据处理进行操作。LoRa 在低功耗、长距离应用中广受欢迎,但缺点是传输时间长,适合 EEW 等时间关键型任务。我们的网络通过多个低扩频因子 (SF) 的短跳广播 EEW 来克服这一限制。该网络包括生成警告的终端节点和广播警告的中继节点。与具有互联网连接的 CRISiSLab 的 EEW 系统性能进行模拟基准测试表明,SF 为 8 可以在 2.4 秒内在 30 公里市区的所有传感器上传播警告。这种方法还具有弹性,因为消息传输可以使用多个路由。我们基于 LoRa 的系统实现了 1-6 秒的警告窗口,略低于 CRISiSLab 系统基于互联网的性能 1.5-6.75 秒。尽管如此,我们的新型网络对于及时的心理准备、简单的保护行动和自动化是有效的。Lilygo LoRa32 原型器件的实验作为实际演示。

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