A Comprehensive Review of Deep Compressive Sensing for Efficient IoT Data Management

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Year : 2024 | Volume :14 | Issue : 03 | Page : –
By
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Hemant Rajoriya,

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Dr. Balajee Sharma,

  1. Assistant professor, Department of Electronics & Communication Engineering, Ram Krishna Dharmarth Foundation University, Bhopal, Madhya Pradesh, India.
  2. Assistant professor, Department of Electronics & Communication Engineering, Ram Krishna Dharmarth Foundation University, Bhopal, Madhya Pradesh, India.

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The Internet of Things has revolutionized data-driven ecosystems and offers advanced services, such as live monitoring and automation in various domains: smart cities, healthcare, and industrial automation. However, with the exponential growth of IoT devices, comes a large amount of data generation, which poses considerable problems like network congestion, latency, and energy inefficiency. Compressive sensing (CS), one of the newest signal processing methodologies, has emerged as an enabler to meet these challenges as it reduces the requirement of data acquisition and transmission without moving away from its signal fidelity. Moreover, integration of CS with deep learning enhances the utility as it enables efficient reconstruction and adaptive processing within the IoT network. This review analyzes the basics of CS, particularly its applicability to IoT context regarding optimizing data management and overcoming limitations of resources. Real-time applications such as traffic monitoring through smart cities and biometric signal transmission in healthcare discuss how robust the framework can be for a CS-based approach. Moreover, the study delves into the idea about deep learning and reconstruction accuracy and scalability – which is still unbound to create an even more intelligent system for IoT applications. CS and deep learning form an essential convergence in managing data overhead, optimizing energy and bandwidth usage, and paving the way for sustainable IoT ecosystems.

Keywords: Internet of Things (IoT), Compressive Sensing (CS), Deep Learning, Data Traffic Optimization, Energy Efficiency, Signal Reconstruction

[This article belongs to Trends in Electrical Engineering (tee)]

How to cite this article:
Hemant Rajoriya, Dr. Balajee Sharma. A Comprehensive Review of Deep Compressive Sensing for Efficient IoT Data Management. Trends in Electrical Engineering. 2024; 14(03):-.
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Hemant Rajoriya, Dr. Balajee Sharma. A Comprehensive Review of Deep Compressive Sensing for Efficient IoT Data Management. Trends in Electrical Engineering. 2024; 14(03):-. Available from: https://journals.stmjournals.com/tee/article=2024/view=0

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Regular Issue Subscription Review Article
Volume 14
Issue 03
Received 16/10/2024
Accepted 12/12/2024
Published 19/12/2024
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