基于LoRaWAN的雾计算深度学习在冷链温度预测中的实际部署与性能表征
Real-World Deployment and Performance Characterisation of Fog-Based Deep Learning for Cold-Chain Temperature Prediction over LoRaWAN
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中文总结 AI 辅助
本文首次在真实冷链场景中部署基于LoRaWAN的雾计算LSTM-GRU温度预测模型，在树莓派4上实现MAE 0.2°C、能耗0.2kWh/天，并验证了可解释性与系统韧性。
中文摘要 AI 辅助
新鲜水果和蔬菜（FFVs）极易腐烂，冷链断裂是造成全球食物浪费的重要因素。虽然机器学习（ML）能够实现主动干预，但基于云的推理面临延迟和数据丢失等挑战。雾计算解决了这些问题，但此前仅在仿真环境中测试过用于FFV冷链温度预测。据作者所知，本文首次实现了其实际部署。一个部署在雾端的LSTM-GRU模型，利用从南非苹果冷藏设施采集的LoRaWAN传感器数据（含人为诱导的冷链断裂）预测冷藏室温度。系统完全运行在树莓派4上，不依赖云，仅在预测到断裂时生成条件SHAP解释。部署系统预测冷藏室温度的MAE为0.2°C，每天能耗约0.2千瓦时（每次预测0.7瓦时）。预测在不到一秒（555毫秒）内完成，时间主要消耗在网络和消息传递而非计算上，条件解释增加了适度开销。SHAP消耗的CPU多28%，但完全在硬件能力范围内。模型主要将预测归因于温度、湿度及其交互作用。关键的是，部署暴露了仿真无法发现的问题：传感器触发的单点故障，以及真正的韧性——从基础设施故障中自主恢复，并在互联网中断时持续运行。这些是FFV冷链中基于雾的温度预测的首批已发表部署基准，确立了在资源受限的边缘硬件上进行可解释温度预测的可行性。未来工作包括异步传感器融合、商业冷链部署、替代模型架构和因果分析。
英文摘要
Fresh fruits and vegetables (FFVs) are highly perishable, and cold-chain breaks contribute significantly to global food waste. While Machine Learning (ML) can enable proactive intervention, cloud-based inference faces challenges such as latency and data loss. Fog computing addresses these issues but has been tested only in simulation for FFV cold-chain temperature prediction. To the best of the authors' knowledge, this paper presents its first real-world deployment. A fog-deployed LSTM-GRU model predicted cold-room temperature using LoRaWAN sensor data collected from a South African apple cold-storage facility with induced cold-chain breaks. Running entirely on a Raspberry Pi 4 with no cloud dependency, the system generated conditional SHAP explanations only when a break is predicted. The deployed system predicts cold-room temperature with an MAE of 0.2°C at roughly 0.2 kWh per day ($\approx 0.7$ Wh per prediction). Predictions were delivered in under one second (555 ms), dominated by network and messaging rather than computation, with conditional explanations adding modest cost. SHAP consumes 28% more CPU but is well within the hardware's capacity. The model attributes its predictions primarily to temperature, humidity, and their interaction. Critically, the deployment surfaced what simulation cannot: a sensor-triggered single point of failure, alongside genuine resilience, autonomous recovery from infrastructure faults and continued operation through internet loss. These are the first published deployment benchmarks for fog-based temperature prediction in FFV cold chains, establishing that explainable temperature forecasting is feasible on resource-constrained edge hardware. Future work includes asynchronous sensor fusion, commercial cold chain deployment, alternative model architectures, and causal analysis.
发表机构
- University of the Western Cape（西开普大学）
机构由 AI 辅助整理，请以论文原文为准。
