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Node Deployment Algorithm Based on Connected Tree for Underwater Sensor Networks.

Peng Jiang1, Xingmin Wang2, Lurong Jiang3

  • 1Key Lab for IOT and Information Fusion Technology of Zhejiang, Hangzhou Dianzi University, Hangzhou 310018, China. pjiang@hdu.edu.cn.

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Summary
This summary is machine-generated.

A new Connected Tree Depth Adjustment (CTDA) algorithm improves underwater sensor network connectivity and coverage while reducing energy consumption compared to the Self-Deployment Depth Adjustment (SDDA) algorithm.

Keywords:
3D coverageconnected treenode depth adjustmentunderwater sensor network deployment

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Area of Science:

  • Underwater sensor networks
  • Network deployment strategies
  • Wireless communication

Background:

  • Efficient deployment is crucial for optimal monitoring in underwater sensor networks.
  • Current methods often involve adjusting node depths, with the Self-Deployment Depth Adjustment (SDDA) algorithm focusing on maximizing coverage.
  • SDDA's connectivity performance, however, is inconsistent.

Purpose of the Study:

  • To propose a novel depth adjustment algorithm, Connected Tree Depth Adjustment (CTDA), to enhance network connectivity and coverage in underwater sensor networks.
  • To address the connectivity limitations of existing algorithms like SDDA.
  • To optimize energy consumption during network deployment.

Main Methods:

  • The CTDA algorithm utilizes a sink node as the root to construct a connected tree, organizing the network into a forest for sustained connectivity.
  • It reduces coverage overlaps within sub-trees by adjusting parent-child node depths and employs a hierarchical strategy to minimize node movement.
  • A silent mode is incorporated to decrease communication costs.

Main Results:

  • CTDA demonstrates superior connectivity compared to SDDA across various communication ranges and node densities.
  • CTDA achieves coverage comparable to SDDA with reduced energy consumption.
  • In sparse environments, CTDA significantly outperforms SDDA in connectivity and energy efficiency.
  • CTDA's performance is similar to Connected Dominating Set algorithms but with lower energy usage, especially in sparse conditions.

Conclusions:

  • CTDA offers a robust solution for underwater sensor network deployment, balancing high connectivity and coverage with reduced energy expenditure.
  • The algorithm's hierarchical approach and silent mode contribute to its efficiency, particularly in challenging sparse environments.
  • CTDA presents a viable alternative to existing depth adjustment algorithms, especially when energy conservation is a priority.