Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Therefore, in this article, we first introduce deep learning for IoTs into the edge computing environment. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. In the performance evaluation, we test the performance of executing multiple deep learning tasks in an edge computing environment with our strategy. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT.

中文翻译：

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在Edge中学习IoT：使用Edge Computing进行物联网深度学习

深度学习是一种有前途的方法，可以从复杂环境中部署的IoT设备的原始传感器数据中提取准确的信息。由于其多层结构，深度学习也适用于边缘计算环境。因此，在本文中，我们首先将IoT的深度学习引入边缘计算环境。由于现有边缘节点的处理能力有限，我们还设计了一种新颖的卸载策略，以通过边缘计算优化IoT深度学习应用程序的性能。在性能评估中，我们使用我们的策略来测试在边缘计算环境中执行多个深度学习任务的性能。评估结果表明，我们的方法在物联网深度学习方面优于其他优化解决方案。