A Comprehensive Review of Compressive Sensing Frameworks for Internet of Things-Enabled Wireless Sensor Networks
DOI:
https://doi.org/10.29304/jqcsm.2026.18.22772Keywords:
Compressive sensing, sparsity, CS acquisition strategies, reconstruction algorithms, CS applicationsAbstract
Compressive sensing (CS) is important in applications as diverse as magnetic resonance imaging (MRI), high-speed communications and high-speed video imaging because it allows signals to be sampled at rates significantly lower than the Nyquist rate thanks to the sparsity of the signals. The study of CS from a signal processing, imaging, and mathematical perspective has led to many studies to go deeper into this topic and understand its pros and cons. We investigate power consumption, communication load, memory requirements, computational complexity, scalability and peripheral feasibility in intensive manner for the technologies. These are more important issues than in the conventional CS research in IoT applications, where wireless sensor networks are employed. Moreover, most existing research works are conducted on a single method for CS, such as sensing arrays, sparse representation, reconstruction, compression, or reconstruction based on deep learning techniques without any prior knowledge. In this review, another factor considered is that a complete framework for compressed sensing must include three key aspects – measurement (sensing) methodologies, sparse representation methodologies, and reconstruction methodologies. In the paper the interactions of compressed sensing techniques have been discussed in the context of the IoT and wireless sensor networks. It makes a full assessment of the latest strategies which have been developed between 2021 and 2025. Besides, we go over existing practical examples of compressed sensing and applications with special focus on the wireless sensor networks (WSN) in IoT. The use of modern methods such as deep learning and hybrid methods is very efficient, useful, and precise; however, it has its practical limitations, especially with high training costs, large memory requirements and power limitations. Finally, the synthesis of the latest literature shows that the current adaptive CS schemes offer a substantial saving in energy consumption (up to 40-50%) and communication overhead, with higher reconstruction quality (PSNR=38-48dB), than the classical iterative deep learning schemes for low sampling frame rate and low computation cost. Finally, this review emphasizes the importance of future research on lightweight, hardware-friendly co-design strategies that will maintain a balance between power efficiency, accuracy, and scalability issues and make the application of compressed sensing to practice a viable future.
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