用轻量级机器学习优化LoRa频段切换，提升物联网传输性能
Optimizing LoRa for Edge Computing with TinyML Pipeline for Channel Hopping
- 在微控制器上部署TinyML模型预测最佳通信频段
- 相比随机跳频，信号强度提升63%，信噪比提高44%
- 适合边缘计算与城市微农场场景的低功耗物联网应用
我们提出将长距离通信方案LoRa集成至物联网到边缘计算系统的数据传输中，利用其免许可频段特性及边缘计算中常见的开源实现。设计了一种频段跳变优化模型，并基于TinyML构建了适用于LoRa传输的频段跳变机制，同时实验研究了一种快速预测算法以识别边缘设备与物联网设备间的空闲信道。在包含LoRa、TinyML和物联网-边缘-云连续体的开源实验环境中，我们构建了一个结合微型农业与城市计算概念的植物推荐应用案例。在优化后的LoRa边缘计算系统中，我们设计了应用工作流，并在采集的应用数据上应用协同过滤与多种机器学习算法，以识别并推荐特定城市微农场的种植计划。在LoRa实验中，通过随机跳频方案对比，测量了包丢失率、接收信号强度指示（RSSI）和信噪比（SNR）。结果表明，在微控制器上使用TinyML进行频段跳变是可行的，且能有效学习最优信道选择策略，相比随机跳频机制，最大可提升RSSI达63%，提升SNR达44%。
原文摘要 · Abstract (English)
We propose to integrate long-distance LongRange (LoRa) communication solution for sending the data from IoT to the edge computing system, by taking advantage of its unlicensed nature and the potential for open source implementations that are common in edge computing. We propose a channel hoping optimization model and apply TinyML-based channel hoping model based for LoRa transmissions, as well as experimentally study a fast predictive algorithm to find free channels between edge and IoT devices. In the open source experimental setup that includes LoRa, TinyML and IoT-edge-cloud continuum, we integrate a novel application workflow and cloud-friendly protocol solutions in a case study of plant recommender application that combines concepts of microfarming and urban computing. In a LoRa-optimized edge computing setup, we engineer the application workflow, and apply collaborative filtering and various machine learning algorithms on application data collected to identify and recommend the planting schedule for a specific microfarm in an urban area. In the LoRa experiments, we measure the occurrence of packet loss, RSSI, and SNR, using a random channel hoping scheme to compare with our proposed TinyML method. The results show that it is feasible to use TinyML in microcontrollers for channel hopping, while proving the effectiveness of TinyML in learning to predict the best channel to select for LoRa transmission, and by improving the RSSI by up to 63 %, SNR by up to 44 % in comparison with a random hopping mechanism.
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