Earticle

초록

영어

The DV-Hoplocalization method has a series of superiorities, such as high distributiveness and expandability, which is perfectly fit for large-scale deployment. Moreover, it may lead to reasonable positioning accuracy merely in isotropous dense network. In practical environment, most scenes are anisotropic, with unevenly distributed nodes. In this paper, kernel PCR method is applied to collect and utilize the correlation between hop count and real distance, so as to build an optimal relationship model, converting hop count information between nodes into the value of real distance, so that DV-Hop method may be applicable to different environment. Compared with existing similar and typical methods, the method proposed in this paper has higher environment adaptability, as well as higher positioning accuracy and stability.

목차

Abstract
 1. Introduction
 2. Background
 3. Relevant Theory Review
  3.1. Kernel Trick
  3.2. Kernel Principal Component Analysis
 4. Localization Algorithm with Kernel Principal Regression
  4.1. Problem Statement
  4.2. Localization Algorithm With KPCR
 5. Performance Evaluation
  5.1. Localization Results with Regularly Deployed Sensors
  5.2. Localization Results with Randomly Deployed Sensors
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Rulin Dou [ College of Information Technology, Jingling Institute of Technology, Nanjing 211169,China(s) ]
• Xiaoyong YAN [ College of Information Technology, Jingling Institute of Technology, Nanjing 211169,China(s) ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605