Indoor localization algorithm based on behavior-driven predictive learning in crowdsourced Wi-Fi environments

Author:

Labinghisa Boney A.1,Lee Dong Myung1

Affiliation:

1. Department of Computer Engineering, Tongmyong University, 428 Sinseon-Ro, Nam-Gu, Busan 48520, Republic of Korea

Abstract

The indoor localization algorithm based on the behavior-driven predictive learning (BDPLA) executes machine-learning predictions by computing the shortest path from a starting location to a destination. The proposed algorithm selects a set of reference points (RPs) to predict the shortest path using all available RPs from the crowdsourced Wi-Fi environment. In addition, the proposed algorithm utilizes the collected received signal strength indicator (RSSI) values to determine the error distance. Using principal component analysis (PCA), the existing crowdsourced RSSI data can be calibrated to help decrease the inconsistent RSSI values among all received signals by reconstructing the values. The average error distance of 3.68 m achieved better results compared with the traditional fingerprint map with an average result of 6.96 m.

Publisher

World Scientific Pub Co Pte Lt

Subject

Condensed Matter Physics,Statistical and Nonlinear Physics

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1. Optimal Crop Selection Using Gravitational Search Algorithm;Mathematical Problems in Engineering;2021-04-19

2. 3D Point Cloud-Based Indoor Mobile Robot in 6-DoF Pose Localization Using a Wi-Fi-Aided Localization System;IEEE Access;2021

3. A Crowdsourcing-based Localization Scheme with Ultra-Wideband Communication;IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society;2020-10-18

4. A novel approach for finding crucial node using ELECTRE method;International Journal of Modern Physics B;2020-04-10

5. Discrete Hopfield neural network based indoor Wi-Fi localization using CSI;EURASIP Journal on Wireless Communications and Networking;2020-04-05

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