Abstract
In this letter, a novel deep learning-based IoT device transmission interval management scheme for enhanced scalability that reduces the redundancy data measurement in LoRa networks is proposed. For this purpose, a Local LSTM prediction model of each cluster is proposed in which the devices are clustered based on the features of the extracted data using an autoencoder. By adjusting the device transmission interval based on the prediction results, the amount of redundantly collected traffic in the LoRa environment is reduced. The proposed scheme is validated using a simulation-based experiment with the Intel lab IoT dataset. Here, we consider the physical characteristics of LoRa and the data pattern of Intel lab data. As a result, the scalability of the proposed scheme can be improved by 31% on average with a 0.3 MAPE prediction error threshold compared to the base model to which the proposed scheme is not applied.
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