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Deep Learning Semantic Compression: IoT Support over LORA Use Case

机译:深度学习语义压缩:基于LORA用例的物联网支持

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Long Range (LORA) networks are serious candidates to support Internet of Things (IoT). Despite the scalability and range of LORA, yet many IoT devices need to send much more data than what is possible in this band. In this paper, a deep learning compression method to squeeze data and transfer its semantic is presented. Data from IoT equipment is considered as a time series and trains a neural network. Resulting neural network weights are periodically sent instead of sending all the IoT raw data. Anomalies are locally detected by a similar neural network and sent separately. The resulting architecture makes it feasible to use LORA for IoT devices that generate very large amounts of data.
机译:远程(LORA)网络是支持物联网(IoT)的重要候选者。尽管LORA具有可扩展性和范围,但是许多IoT设备需要发送的数据量远远超过该频段中的数据量。本文提出了一种深度学习压缩方法,用于压缩数据并传递其语义。来自物联网设备的数据被视为一个时间序列,并训练一个神经网络。定期发送结果神经网络权重,而不是发送所有IoT原始数据。异常由类似的神经网络在本地检测并单独发送。由此产生的架构使将LORA用于生成大量数据的IoT设备变得可行。

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