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Indoor localization using K-nearest neighbor and artificial neural network back propagation algorithms | IEEE Conference Publication | IEEE Xplore

Indoor localization using K-nearest neighbor and artificial neural network back propagation algorithms


Abstract:

Currently, indoor localization plays vigorous role in academia and industries. In the proposed technique, 99.78% of room level classifications are correctly classified us...Show More

Abstract:

Currently, indoor localization plays vigorous role in academia and industries. In the proposed technique, 99.78% of room level classifications are correctly classified using K-nearest Neighbor (KNN). For regression based problem, an Artificial Neural Network in Back Propagation (ANNBP) performs an accuracy of 50% and 100% for errors less than 0.5 m and 0.9 m respectively. The root-mean-square error (RMSE) for regression based localization is 0.56. Thus, the result confirmations that the integration of KNN with ANNBP techniques can give better indoor location based services (LBSs).
Date of Conference: 30 April 2018 - 01 May 2018
Date Added to IEEE Xplore: 07 June 2018
ISBN Information:
Electronic ISSN: 2379-1276
Conference Location: Hualien, Taiwan

References

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