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

Mingqiang Zhang

doi:10.1109/JIOT.2021.3059735

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

2021-07-15

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DOI：10.1109/JIOT.2021.3059735

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Abstract

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Due to the booming of various devices in Internet-of-Things (IoT) networks, more data should be transmitted over the networks, which will thereby consume 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, and 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 CS and deep learning. Our objective is to reduce the volume of data to be transmitted over IoT networks without losing reconstruction accuracy. A deep CS network 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 data sets with different structured sparse models and a real sensor data set are utilized in doing experiments. The performance of the proposed scheme in terms of mean-squared error, peak-signal-noise-ratio, and structural similarity is investigated. The results demonstrate the effectiveness of the proposed scheme in reconstruction accuracy for given compression ratio. The results also show that the proposed scheme is suitable for the process of CDA, thus can effectively reduce the amount of data to be transmitted in IoT networks.

Keywords:

Data aggregation

Internet of Things

Sparse matrices

Data communication

Reconstruction algorithms

Matching pursuit algorithms

Data acquisition

Compressed data aggregation (CDA)

compressed sensing (CS)

data reconstruction

deep learning

Internet of Things (IoT)

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