Earticle

초록

영어

Location estimation (LE) is important in Ubiquitous Computing Environments (UCE). For positioning application, Global Positioning System (GPS) and Received Signal Strength (RSS) from Access Pointer (AP) using LE is increasingly popular choice especially after pervasive adoption of IEEE 802.11 Wireless Sensor Networks (WSN). Fundamental requirement of such LE is to estimate location from RSS at a particular location. Multi-Path Fading Effects (MPFE) make RSS to fluctuate in unpredictable manner, introducing uncertainty in LE. Moreover, in practical situations, RSS values RF Signal (RFS) from AP are not available at some locations all the time making the problem more difficult. To deal with this problem, machine learning techniques have been applied so that the carried along devices can learn and make decision where they are in the building, especially. Recent machine learning techniques remain many unsolved problem such as high cost of computation, high complexity of model structures and scalability. In this paper, we will introduce a few methods which give high accuracy and overcome other methods’ disadvantages, such as Support Vector Machine (SVM), Push Pull Estimation (PPE), and Modular Multi-Layer Perceptron (MMLP) with Neural Networks (NN) using RFS from AP.

목차

Abstract
 1. Introduction
  1.1. Location Estimation(LE)
  1.2. Interest for LE
  1.3. Received Signal Strength (RSS)
  1.4. Machine Learning Techniques
 2. Methods of Research
  2.1. LE based on MMLP
  2.2. The First Phase of PPE in NN based on LE
 3. Experiment and Discussions
  3.1. Data Collecting Methodology
  3.2. The NN and First Phase of PPE based on LE
 4. Proposed Idea and Analysis for Collaborative Scheme
  4.1. Statement 1
  4.2. Statement 2
  4.3. Statement 3
  4.4. Advantages
  4.5. Disadvantages
 5. Conclusion
 Acknowledgements
 References

키워드

LE RSS GPS SVM MMLP PPE WSN MPFE NN UCE AP RFS

저자

• Seong-Jin Cho [ School of IT Convergence Engineering, Shinhan University, Republic of Korea ]
• Ho-Kyun Park [ School of IT Convergence Engineering, Shinhan University, Republic of Korea ] Corresponding author

참고문헌

발행기관

간행물