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Deterministic clustering based compressive sensing scheme for fog-supported heterogeneous wireless sensor networks.

Walid Osamy1,2, Ahmed Aziz1,3,4, Ahmed M Khedr5,6

  • 1Computer Science Department, Faculty of Computers and Artificial intelligence, Benha University, Benha, Egypt.

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Summary

This study introduces a deterministic clustering-based compressive sensing (CS) scheme for Wireless Sensor Networks (WSNs) in the Internet of Things (IoT). The DCCS method enhances data acquisition, reduces energy use, and improves CS reconstruction for longer network life.

Keywords:
CS reconstruction algorithmsCompressive sensingFog networkIoTWSNs

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Data acquisition in large-scale Wireless Sensor Networks (WSNs) is a significant challenge for Internet of Things (IoT) evolution.
  • Integrating Compressive Sensing (CS) with routing protocols is a promising area, but effective task-specific integration remains an open question.

Purpose of the Study:

  • To address the data acquisition problem in fog-supported heterogeneous WSNs.
  • To introduce an effective deterministic clustering-based CS scheme (DCCS) for WSNs.
  • To enhance CS data reconstruction and prolong network lifetime.

Main Methods:

  • The proposed Deterministic Clustering based CS (DCCS) scheme utilizes fog computing principles.
  • DCCS simplifies network self-organization, reducing overhead and computational costs.
  • CS is applied at each sensor node to minimize energy expenditure, coupled with a Random Selection Matching Pursuit (RSMP) algorithm for efficient data reconstruction at the base station.

Main Results:

  • The DCCS scheme effectively minimizes overall network power consumption.
  • The proposed method significantly prolongs the operational lifetime of IoT networks.
  • The RSMP algorithm demonstrates improved performance in CS data reconstruction.

Conclusions:

  • The DCCS scheme offers an effective solution for data acquisition challenges in fog-supported heterogeneous WSNs.
  • Integrating fog computing, CS, and an optimized reconstruction algorithm (RSMP) enhances WSN performance and longevity.
  • The study validates the proposed technique's ability to reduce energy expenditure and improve data recovery in IoT networks.