Deep Learning-Enhanced Compressive Sensing for Wireless IoT Data Optimization and Weather Monitoring

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Year : 2024 | Volume :02 | Issue : 02 | Page : 19-35
By
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Hemant Rajoriya,

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Balajee Sharma,

  1. Assistant Professor, Department of Electronics and Telecommunication Engineering, RKDF University, Bhopal, Madhya Pradesh, India
  2. Assistant Professor, Department of Electronics and Telecommunication Engineering, RKDF University, Bhopal, Madhya Pradesh, India

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This research explores the application of deep learning and compressive sensing in order to optimize data traffic in NOMA-based Wireless Internet of Things (IoT) networks and weather monitoring. Such a framework would be very effective and overcome pilot attacks and reconstruction losses for secure data transmission. In this regard, a strong communication model has been adopted based on power-domain NOMA for simultaneous wireless transmission by multiple IoT devices. The system utilizes spreading codes based on advanced transformations to prevent collisions in signals. It uses a novel Deep Compressive Sensing algorithm, which employs a combination of greedy reconstruction techniques, like CoSAMP, with deep learning to improve the accuracy with which signals are reconstructed. It ensures resilience against noise and pilot contamination attacks while it maintains very low latency and high accuracy in methodology. Simulations run on MATLAB confirm the model with substantial enhancements regarding MSE and RMSE performances from various SNRs as well as compression ratios. This study introduces an improved compressive sensing framework for weather data acquisition, utilizing deep learning-driven reconstruction algorithms. The proposed method enhances the sampling process, minimizes the data needed for precise reconstruction, and boosts the efficiency of weather monitoring systems. Through comprehensive simulations and analysis of real-world data, the performance of the suggested approach is assessed, showing notable advancements in reconstruction precision and computational efficiency over traditional methods. These results suggest promising prospects for the development of scalable, real-time, and cost-effective weather monitoring solutions. Extensive scaleability, robustness, and high potential to meet the stringent demands of next-generation IoT networks come out clearly in the results. Future research directions would span the model over multi-hop IoT environments, and blockchain-based security mechanisms would be integrated.

Keywords: Wireless IoT, deep learning, compressive sensing, Noma, pilot attack, signal reconstruction

[This article belongs to International Journal of Satellite Remote Sensing (ijsrs)]

How to cite this article:
Hemant Rajoriya, Balajee Sharma. Deep Learning-Enhanced Compressive Sensing for Wireless IoT Data Optimization and Weather Monitoring. International Journal of Satellite Remote Sensing. 2024; 02(02):19-35.
How to cite this URL:
Hemant Rajoriya, Balajee Sharma. Deep Learning-Enhanced Compressive Sensing for Wireless IoT Data Optimization and Weather Monitoring. International Journal of Satellite Remote Sensing. 2024; 02(02):19-35. Available from: https://journals.stmjournals.com/ijsrs/article=2024/view=0

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Regular Issue Subscription Original Research
Volume 02
Issue 02
Received 23/11/2024
Accepted 25/11/2024
Published 05/12/2024
296