Compressive Sensing and Autoencoder Based Compressed Data Aggregation for Green IoT Networks

In cellular Internet of Things networks, massive access and highly dynamic traffic of machine type communication devices may cause network congestion, heavy energy consumption or even service unavailability. To reduce the amount of data transmission and improve the efficiency of data aggregation in sporadic machine type communications, we develops a two-stage compressed data aggregation scheme by jointly utilizing compressive sensing and sparse autoencoder. A deep compressive sensing network (DCSNet) is designed by using deep learning method to reconstruct signals from compressive measurements. Experimental results demonstrate that, our scheme can effectively reduce the data traffic, achieve higher reconstruction accuracy in different sparse conditions of signal and brake the obstacles that compressive sensing method has a large reconstruction error when the measured data is small.