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Sensors Network Optimization by a Novel Genetic Algorithm

  • Hui Wang
  • Anna L. Buczak
  • Hong Jin
  • Hongan Wang
  • Baosen Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3222)

Abstract

This paper describes the optimization of a sensor network by a novel Genetic Algorithm (GA) that we call King Mutation C2. For a given distribution of sensors, the goal of the system is to determine the optimal combination of sensors that can detect and/or locate the objects. An optimal combination is the one that minimizes the power consumption of the entire sensor network and gives the best accuracy of location of desired objects. The system constructs a GA with the appropriate internal structure for the optimization problem at hand, and King Mutation C2 finds the quasi-optimal combination of sensors that can detect and/or locate the objects. The study is performed for the sensor network optimization problem with five objects to detect/track and the results obtained by a canonical GA and King Mutation C2 are compared.

Keywords

Genetic Algorithm Sensor Network Object Tracking Reproduction Process Network Objective 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© IFIP International Federation for Information Processing 2004

Authors and Affiliations

  • Hui Wang
    • 1
  • Anna L. Buczak
    • 2
  • Hong Jin
    • 1
  • Hongan Wang
    • 1
  • Baosen Li
    • 3
  1. 1.Institute of SoftwareChinese Academy of SciencesChina
  2. 2.Lockheed Martin Advanced Technology LaboratoriesCherry HillUSA
  3. 3.Zibo Electric Power CompnayZibo.ShandongChina

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