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.

翻译：我们提出集成长距离LoRa通信解决方案，将物联网数据传输至边缘计算系统，充分利用其免许可特性及开源实现的潜力——这在边缘计算领域已形成普遍实践。我们构建了信道跳频优化模型，应用基于TinyML的信道跳频模型优化LoRa传输，并通过实验研究快速预测算法以寻找边缘设备与物联网设备间的空闲信道。在包含LoRa、TinyML及物联网-边缘-云连续体的开源实验平台中，我们以植物推荐应用为案例，整合了新型应用工作流与云友好协议解决方案，该案例融合了微农业与城市计算理念。在LoRa优化的边缘计算架构中，我们设计了应用工作流，对采集的应用数据实施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，我们测量了采用随机信道跳频方案时的丢包率、RSSI与SNR指标，并与提出的TinyML方法进行对比。结果表明：在微控制器中应用TinyML实现信道跳频具备可行性，TinyML能有效学习预测LoRa传输最优信道选择，相较于随机跳频机制，RSSI提升最高达63%，SNR提升最高达44%。