Rapid growth in numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power, and thus, is not well-suited for ML tasks. Consequently, this article aims to combine edge and cloud computing for IoT data analytics by taking advantage of edge nodes to reduce data transfer. In order to process data close to the source, sensors are grouped according to locations, and feature learning is performed on the close by edge node. For comparison reasons, similarity-based processing is also considered. Feature learning is carried out with deep learning - the encoder part of the trained autoencoder is placed on the edge and the decoder part is placed on the cloud. The evaluation was performed on the task of human activity recognition from sensor data. The results show that when sliding windows are used in the preparation step, data can be reduced on the edge up to 80% without significant loss in accuracy.

中文翻译：

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用于物联网数据分析的边缘云计算：通过深度学习将智能嵌入边缘


包括传感器、移动设备、可穿戴设备和其他物联网 (IoT) 设备在内的联网设备数量快速增长，导致网络上移动的数据呈爆炸式增长。为了进行机器学习 (ML)，物联网数据通常会传输到云端或另一个集中式系统进行存储和处理；然而，这会导致延迟并增加网络流量。边缘计算有可能通过将计算移近网络边缘和数据源来解决这些问题。另一方面，边缘计算在计算能力方面受到限制，因此不太适合机器学习任务。因此，本文旨在通过利用边缘节点减少数据传输，将边缘计算和云计算结合起来进行物联网数据分析。为了处理靠近源的数据，传感器根据位置进行分组，并在靠近边缘的节点上进行特征学习。出于比较的原因，还考虑了基于相似性的处理。特征学习是通过深度学习进行的——训练好的自动编码器的编码器部分放置在边缘，解码器部分放置在云端。该评估是针对从传感器数据识别人类活动的任务进行的。结果表明，当在准备步骤中使用滑动窗口时，边缘数据可以减少高达 80%，而不会显着损失准确性。