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E2JSL: Energy Efficient Joint Time Synchronization and Localization Algorithm Using Ray Tracing Model.

Rehan Shams1, Pablo Otero2, Muhammad Aamir1

  • 1Department of Telecommunication, Sir Syed University of Engineering and Technology, Karachi 75300, Pakistan.

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|December 22, 2020
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Summary
This summary is machine-generated.

This study introduces an energy-efficient framework for joint localization and time synchronization in underwater wireless sensor networks (UWSNs). The approach improves accuracy and reduces energy consumption for underwater sensors.

Keywords:
energy efficiencylocalizationray tracingstratification effecttime synchronization

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Area of Science:

  • Marine Technology
  • Sensor Networks
  • Underwater Acoustics

Background:

  • Localization and time synchronization in underwater wireless sensor networks (UWSNs) are critical but typically addressed separately.
  • Independent approaches lead to suboptimal energy efficiency and accuracy for underwater sensor nodes.

Purpose of the Study:

  • To develop an energy-efficient joint framework for localization and time synchronization in UWSNs.
  • To enhance the accuracy and reduce the energy consumption of underwater sensor networks.

Main Methods:

  • A novel joint framework integrating localization and time synchronization for UWSNs.
  • Utilizing a ray-tracing approach based on Snell's law to account for sound speed variations and stratification effects.
  • Implementing an iterative process to refine localization and time synchronization accuracy.

Main Results:

  • The proposed joint approach demonstrates superior performance in both energy efficiency and accuracy compared to existing methods.
  • Simulation results validate the effectiveness of the joint framework.
  • Cramer-Rao lower bound analysis confirms the convergence of the proposed technique.

Conclusions:

  • The developed energy-efficient joint framework offers significant improvements for UWSNs.
  • The integration of localization and time synchronization is crucial for optimizing underwater sensor network performance.
  • The method provides a computationally efficient solution with reduced running time.