Applying Deep Neural Network (DNN) for large-scale indoor localization using feed-forward neural network (FFNN) algorithm

Abebe Belay Adege,Yirga Yayeh,Lei Yen,Shiann-Shiun Jeng,Hsin-Piao Lin,Getaneh Berie,Yun Ruei Li

doi:10.1109/icasi.2018.8394387

Applying Deep Neural Network (DNN) for large-scale indoor localization using feed-forward neural network (FFNN) algorithm

Abebe Belay Adege, Yirga Yayeh + Show 5 more

https://doi.org/10.1109/icasi.2018.8394387

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Publication Date: Apr 1, 2018

Citations: 21

Affiliation: National Taipei University of Technology, National Dong Hwa University

#Feed-forward Neural Network #Location-based Mobile Advertising + Show 8 more

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Abstract

In Internet of things (IoT), indoor localization plays a vital role in everyday applications such as locating mobile users, location-based mobile advertising and requesting nearest business. Received Signal Strength (RSS) is used due to minimum cost, less operational complexity, and easy usages. In this work, we proposed a Feed-Forward Deep Neural Network (FF-DNN) Algorithm. The simulation result shows 53.123%, 78.123% and 100% of the accuracy has estimation error less than 0.5, 0.75 and 1 meters respectively. The RMSE is 0.32.

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