The merging boundaries between edge computing and deep learning are forging a new blueprint for the Internet of Things (IoT). However, the low-quality of data in many IoT platforms, especially those composed of heterogeneous devices, is hindering the development of high-quality applications for those platforms. The solution presented in this article is intelligent data collaboration, i.e., the concept of deep learning providing IoT with the ability to adaptively collaborate to accomplish a task. Here, we outline the concept of intelligent data collaboration in detail and present a mathematical model in general form. To demonstrate one possible case where intelligent data collaboration would be useful, we prepared an implementation called adaptive data cleaning (ADC), designed to filter noisy data out of temperature readings in an IoT base station network. ADC primarily consists of a denoising autoencoder LSTM for predictions and a four-level data processing mechanism to perform the filtering. Comparisons between ADC and a maximum slop method show ADC with the lowest false error and the best filtering rates.

中文翻译：

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异构设备物联网平台中的智能数据协作

边缘计算和深度学习之间的融合边界正在打造物联网 (IoT) 的新蓝图。然而，许多物联网平台，尤其是由异构设备组成的平台，数据质量低下，阻碍了这些平台的高质量应用程序的开发。本文提出的解决方案是智能数据协作，即深度学习的概念为物联网提供了自适应协作完成任务的能力。在这里，我们详细概述了智能数据协作的概念，并以一般形式呈现了一个数学模型。为了演示智能数据协作可能有用的一种情况，我们准备了一种称为自适应数据清理 (ADC) 的实现，旨在从物联网基站网络中的温度读数中滤除噪声数据。ADC 主要由用于预测的去噪自动编码器 LSTM 和用于执行过滤的四级数据处理机制组成。ADC 和最大斜率方法之间的比较表明 ADC 具有最低的错误误差​​和最佳的过滤率。