Learning-based Sparse Data Reconstruction for Compressed Data Aggregation in IoT Networks

As the booming of various devices in IoT networks, more data should be transmitted over the networks, which will thereby comsume more transmission bandwidth and more transmit power. Compressed data aggregation (CDA) has been proposed as an effective way to reduce the amount of the collected data in IoT networks. Although adopting compressed sensing (CS), CDA can sample the source data efficiently, sparse data reconstruction is still a big challenge. In this work, inspired by this, we propose a learning-based sparse data reconstruction scheme by jointly utilizing compressed sensing and deep learning. Our objective is to reduce the volume of data to be transmitted over IoT networks without losing reconstruction accuracy. A deep compressed sensing network (DCSNet) is designed by adopting an end-to-end learning method to build a measurement matrix and an efficient and high-accuracy reconstruction network. To show the performance of the proposed scheme, six datasets with different structured sparse models and a real sensor dataset are utilized in doing experiments. Performance of the proposed scheme in terms of mean squared error (MSE), peak-signal-noise-ratio (PSNR), and structural similarity (SSIM) are investigated. Results demonstrate the effectiveness of the proposed scheme in reconstruction accuracy for given compression ratio (CR). The results also show that the proposed scheme is suitable for the process of compressed data aggregation, thus can effectively reduce the amount of data to be transmitted in IoT networks.