Abstract

The emerging compressed sensing (CS) theory can significantly reduce the number of sampling points that directly corresponds to the volume of data collected, which means that part of the redundant data is never acquired. It makes it possible to create standalone and net-centric applications with fewer resources required in Internet of Things (IoT). CS-based signal and information acquisition/compression paradigm combines the nonlinear reconstruction algorithm and random sampling on a sparse basis that provides a promising approach to compress signal and data in information systems. This paper investigates how CS can provide new insights into data sampling and acquisition in wireless sensor networks and IoT. First, we briefly introduce the CS theory with respect to the sampling and transmission coordination during the network lifetime through providing a compressed sampling process with low computation costs. Then, a CS-based framework is proposed for IoT, in which the end nodes measure, transmit, and store the sampled data in the framework. Then, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and lower energy efficiency. Performance is evaluated with respect to network size using datasets acquired by a real-life deployment.

Highlights

  • Researchers found that in information systems, wireless sensor networks (WSNs), and Internet of Things (IoT), many types of information has a property called sparseness in transformation process which allows certain number of samples enabling capturing all required information without loss of information [1], [2], [3], [4]

  • 2) A compressed sensing (CS)-based information acquisition framework is proposed for IoT, which involves the compressed sampling at IoT end node, information transmission over IoT, and accurate data reconstruction at fusion centre (FC)

  • We first proposed a compressed sensing framework for WSNs and IoT and introduced how the framework could be utilized to reconstruct the sparse or compressible information data into a variety of information systems involving with WSNs and IoT

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Summary

INTRODUCTION

Effectively collect and process the data and information at IoT end nodes [2], [3], [5], [6]. For the first time, our work studies information acquisition in IoT and WSNs with CS from the perspective of data compressed sampling, robust transmission, and accurate reconstruction to reduce the energy consumption, computation costs, data redundancy, and increase the network capacity. This paper considers a particular situation that involves with distributed information sources of data and their acquisition, transmission, storage, and processing in a large-scale IoT [18]. 2) A CS-based information acquisition framework is proposed for IoT, which involves the compressed sampling at IoT end node, information transmission over IoT, and accurate data reconstruction at FC. In this framework, the noise model, communication load, and recovery accuracy are considered for its industrial applications.

COMPRESSED SENSING
Conditions for Compressed Sensing
Reconstruction Algorithms
Noise and Reconstruction Accuracy in Compressed Sensing
CS-BASED FRAMEWORK IN WSNS AND IOT
System Architecture
Sparse Representation
Adaptive Cluster Sparse Representation and Recovery Algorithm
PERFORMANCE EVALUATION
Nodes-Dependent Signal Acquisition
CONCLUSION
Findings
Cooperative Signal and Data Acquisition
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