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Local Coverage Optimization Strategy Based on Voronoi for Directional Sensor Networks.

Guanglin Zhang1,2, Shan You3,4, Jiajie Ren5,6

  • 1College of Information Science and Technology, Donghua University, Shanghai 201620, China. glzhang@dhu.edu.cn.

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|December 22, 2016
PubMed
Summary
This summary is machine-generated.

This study enhances directional sensor network (DSN) coverage by optimizing local cell coverage. Three novel algorithms improve area coverage ratio in randomly distributed DSNs.

Keywords:
Voronoi diagramangle of view (AoV)area coveragedirectional sensor networks (DSNs)wireless sensor networks (WSNs)

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Area of Science:

  • Computer Science
  • Wireless Sensor Networks
  • Network Coverage

Background:

  • Directional sensor networks (DSNs) present unique challenges in area coverage analysis due to factors like random node distribution, sensing radius, working direction, and angle of view (AoV).
  • Traditional methods for analyzing network coverage are often complex and computationally intensive, especially for DSNs with dynamic parameters.

Purpose of the Study:

  • To develop and evaluate efficient algorithms for improving the area coverage of Directional Sensor Networks (DSNs) with random node distribution.
  • To transform the complex global network coverage problem into manageable local cell coverage problems.

Main Methods:

  • Utilized Voronoi diagrams to decompose the network area into individual cells, simplifying the coverage problem.
  • Proposed three decentralized local coverage optimization algorithms: Move Inside Cell (MIC), Rotate Working Direction (RWD), and Rotation based on boundary (RB).
  • Conducted extensive simulations to validate the effectiveness of the proposed algorithms.

Main Results:

  • The proposed algorithms, MIC, RWD, and RB, demonstrated significant improvements in cell coverage.
  • The decentralized approach effectively optimized local coverage, contributing to enhanced overall network area coverage.
  • Simulations confirmed the effectiveness of the algorithms in increasing the coverage ratio.

Conclusions:

  • The proposed local optimization algorithms are effective in improving the area coverage of Directional Sensor Networks.
  • The Voronoi diagram-based approach provides a scalable and efficient method for analyzing and optimizing DSN coverage.
  • These algorithms offer practical solutions for enhancing the performance of DSNs in various applications.