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A target coverage scheduling scheme based on genetic algorithms in directional sensor networks.

Joon-Min Gil1, Youn-Hee Han

  • 1School of Computer and Information Communications Engineering, Catholic University of Daegu, 330 Geumnak-Ri, Hayang-Eup, Gyeongsan-Si, Gyeongbuk 712-702, Korea. jmgil@cu.ac.kr

Sensors (Basel, Switzerland)
|February 10, 2012
PubMed
Summary

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This study introduces a genetic algorithm for directional sensor networks (DSNs) to extend network lifetime. The genetic algorithm outperforms a greedy approach in maximizing coverage and conserving energy for directional sensor networks.

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Wireless Sensor Networks

Background:

  • Directional sensor networks (DSNs) are crucial for real-world monitoring but face challenges with limited battery power and sensing angles.
  • Maximizing network lifetime while ensuring complete target coverage is a significant challenge in DSNs.
  • Node wake-up scheduling protocols are essential for energy conservation in DSNs.

Purpose of the Study:

  • To address the NP-complete Maximum Set Covers for DSNs (MSCD) problem.
  • To propose and evaluate a genetic algorithm-based target coverage scheduling scheme for DSNs.
  • To compare the performance of the proposed genetic algorithm scheme against a greedy algorithm-based scheme.

Main Methods:

  • Formulated the Maximum Set Covers for DSNs (MSCD) problem.
Keywords:
directional sensorsgenetic algorithmsgreedy algorithmsnetwork lifetimetarget coverage

Related Experiment Videos

Last Updated: May 25, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

  • Developed a greedy algorithm-based target coverage scheduling scheme as a baseline.
  • Proposed a novel target coverage scheduling scheme utilizing a genetic algorithm with evolutionary global search.
  • Conducted simulations to verify and evaluate the proposed schemes.

    • Main Results:

    • The genetic algorithm-based scheme effectively finds optimal cover sets for DSNs.
    • Both proposed schemes contribute to extending the network lifetime of DSNs.
    • The genetic algorithm-based scheme demonstrated superior performance in maximizing network lifetime compared to the greedy algorithm-based scheme.

    Conclusions:

    • Genetic algorithms offer a powerful approach for optimizing target coverage scheduling in DSNs.
    • The proposed genetic algorithm-based scheme significantly enhances DSN network lifetime.
    • This research provides an effective solution for energy-efficient target monitoring in DSNs.