LSTM Autoencoders for Internet of Things Data Compression and Battery Conservation

This research focuses on a novel data compression technique in anInternet of Things (IoT) based digital communication system.The work simulated a wireless system being transmitted over a Rayleigh fading channel using Phase Shift Keying (M-PSK).The transmitted data has been sourced from Human Activity Recognition (HAR) using wearable devices and an open-source application.Unlike conventional compression techniques, this study uses Long Short-Term Memory (LSTM) Autoencoders to transform data in low-dimensional form in the wireless system.The main purpose of this study is to evaluate the efficiency of LSTM Autoencoders by reducing data dimensionality while maintaining important information for better activity recognition accuracy.This approach leads to better conservation in IoT devices.The proposed method's performance has been evaluated at various compression levels and modulation levels.The Bit Error Rate (BER) vs. Signal Noise Ratio (SNR) curves of the M-PSK system have been evaluated in comparison with the Mean Square Error (MSE) of the compression and decompression.A trade-off between compression ratio and MSE has been illustrated, which ultimately leads to determining the accuracy of the measurement of human activity.The results highlight the benefits of leveraging the power of LSTM Autoencoders for data compression in the communication of the wireless system.The results contribute to advancing wearable HAR systems and this general performance optimization of the IoT system.