By Topic

The research of the fuzzy cluster algorithm for indoor location based on RSSI

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Dan Liu ; Res. Instn. of Electron. Sci. & Technol., Univ. of Electron. Sci. & Technol. of China, Chengdu, China ; Daoping Tang ; Zhan Xu ; Yue Hu
more authors

In this paper, we introduce a new algorithm λ -WFC based on fuzzy cluster into the indoor location of WSN. First, a location fingerprint database is set up and we calculate an optimal value of λ which is a threshold for classifying the RSS vectors in the off-line training phase. Then in the locating phase, after fuzzy clustering has been done, we estimate the degree of similarity between two vectors by Jffreys&Matusita formula, and assign different weights to the coordinates of reference nodes to calculate the location of the unknown node. Finally it is proved in our simulation experiments that this algorithm could be effective to avoid the dilemma caused by the multipath and abnormal signal attenuation as well as that it has the advantages of low complexity, fast execution, high accuracy and practical applicability for the large-scale of indoor location of WSN.

Published in:

Software Engineering and Service Science (ICSESS), 2012 IEEE 3rd International Conference on

Date of Conference:

22-24 June 2012