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What is Compressive Sensing

Chapter 5
Opportunities and Challenges of Industrial IoT in 5G and 6G Networks
This is a signal processing technique that allows the reconstruction of a signal from only a small number of measurements or samples. It has applications in a variety of fields such as image processing, radar imaging, and medical imaging. The key idea behind compressive sensing is that a signal's sparsity or compressibility in a certain domain can be exploited such that fewer measurements are needed to accurately reconstruct the signal. This reduces data acquisition time and storage requirements, making it a useful tool in situations where data acquisition and storage resources are limited. Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions to underdetermined linear systems. This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Nyquist–Shannon sampling theorem.
Published in Chapter:
Innovative Model of Internet of Things for Industrial Applications
Jay Kumar Jain (Sagar Institute of Research and Technology, India) and Dipti Chauhan (Prestige Institute of Engineering Management and Research, India)
Copyright: © 2023 |Pages: 24
DOI: 10.4018/978-1-7998-9266-3.ch005
Abstract
The internet of things is one of the most significant and promising innovations today. In this chapter, the authors proposed the dual probability-based energy estimation model in the wireless sensor network. The dual probability-based function measures the expected value of energy for the transmission of data. This function creates a subgroup of networks based on energy function and carries out the operation of energy management in the context sensor node data processing. This function also integrates cloud-based services with the sensor networks. The benefit of this function is that it increases the throughput of network and quality of service. The proposed model was simulated in MATLAB R-2014a environment, and the results were obtained using different scenarios of network density. Finally, the authors analyzed the performance of our proposed work with respect to the following metrics: data utility, energy consumptions, and data reconstruction error.
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Chapter 12
Compressive Spectrum Sensing: Wavelet-Based Compressive Spectrum Sensing in Cognitive Radio
A signal processing technique that allows the reconstruction of signals from samples obtained by sampling the signal at a sub-Nyquist rate.
Published in Chapter: Compressive Spectrum Sensing: Wavelet-Based Compressive Spectrum Sensing in Cognitive Radio; From: Sensing Techniques for Next Generation Cognitive Radio Networks
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Chapter 6
A Collaborative Approach for Compressive Spectrum Sensing
CS is a signal processing technique by which the signal can be captured and recovered using only a small number of sub Nyquist measurements under certain conditions.
Published in Chapter: A Collaborative Approach for Compressive Spectrum Sensing; From: Handbook of Research on Software-Defined and Cognitive Radio Technologies for Dynamic Spectrum Management
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