skip to main content

Model Multi Layer Perceptron untuk Indoor Positioning System Berbasis Wi-Fi

Multi Layer Perceptron Model for Indoor Positioning System Based on Wi-Fi

Department of Informatics, Universitas Kristen Duta Wacana, Indonesia

Received: 31 May 2017; Published: 31 Jul 2017.
Open Access Copyright (c) 2017 Jurnal Teknologi dan Sistem Komputer under http://creativecommons.org/licenses/by-sa/4.0.

Citation Format:
Abstract

Indoor positioning system issue is an open problem that still needs some improvements. This research explores the utilization of multilayer perceptron in determining someone’s position inside a building or a room, which generally known as Indoor Positioning System. The research was conducted in some steps: dataset normalization, multilayer perceptron implementation, training process of multilayer perceptron, evaluation, and analysis. The training process has been conducted many times to find the best parameters that produce the best accuracy rate. The experiment produces 79,16% as the highest accuracy rate. Compared to previous research, this result is comparably lower and needs some parameters tweaking or changing the neural networks architectures.

Keywords: indoor positioning system;Wi-Fi positioning; multilayer perceptron
Funding: Fakultas Teknologi Informasi, Universitas Kristen Duta Wacana

Article Metrics:

  1. A. Ozer and E. John, "Improving the Accuracy of Bluetooth Low Energy Indoor Positioning System Using Kalman Filtering," in 2016 International Conference on Computational Science and Computational Intelligence (CSCI), 2016
  2. W. An, Z. Shen and J. Wang, "Compact Low-Profile Dual-Band Tag Antenna for Indoor Positioning Systems," IEEE Antennas and Wireless Propagation Letters, vol. 16, pp. 400-403, 2017
  3. U. Miksaj and D. Bonefacic, "Propagation characteristics of UHF radiofrequency identification system signal for application in indoor positioning," in 2016 22nd International Conference on Applied Electromagnetics and Communications (ICECOM), 2016
  4. Y. See, N. Noor and Calvin Tan Y.M, "Investigation of Indoor Positioning System using Visible Light Communication," in 2016 IEEE Region 10 Conference (TENCON), 2016
  5. L. Chen, Chi-Ren Chen and Da-En Chen, "VIPS: A video-based indoor positioning system with centimeter-grade accuracy for the IoT," in 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2017
  6. A. Ismail, H. Kitagawa, R. Tasaki and K. Terashima, "WiFi RSS fingerprint database construction for mobile robot indoor positioning system," in 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016
  7. C. Ko and S. Wu, "A Proactive Indoor Positioning System in Randomly Deployed Dense WiFi Networks," in 2016 IEEE Global Communications Conference (GLOBECOM), 2016
  8. L. Mainetti, L. Patrono and I. Sergi, “A Survey On Indoor Positioning System,” in 2014 22nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2014
  9. X. Chen and Z. Gao, “Indoor Ultrasonic Positioning System of Mobile Robot Based On TDOA Ranging and Improved Trilateral Algorithm,” in 2017 2nd International Conference on Image, Vision and Computing (ICIVC), 2017
  10. Y. Lukito, and A. R. Chrismanto, “Perbandingan Metode-Metode Klasifikasi Untuk Indoor Positioning System,” Jurnal Teknologi Informasi dan Sistem Informasi, vol. 1, no. 2, pp. 123-131, 2015
  11. M. Abadi et al., “TensorFlow: A System for Large Scale Machine,” in 12th USENIX Symposium on Operating System Design and Implementation (OSDI 16), 2016

Last update:

  1. Comparison of Accuracy and Sensitivity in Liver Cancer Segmentation of Magnetic Resonance Images using Convolutional Neural Network in Comparison with Support Vector Machine

    S. Charan, G. Uganya, M. Naveen Kumar. 2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS), 2022. doi: 10.1109/MACS56771.2022.10023048
  2. Recurrent neural networks model for WiFi-based indoor positioning system

    Yuan Lukito, Antonius Rachmat Chrismanto. 2017 International Conference on Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS), 2017. doi: 10.1109/ICON-SONICS.2017.8267833

Last update: 2024-11-12 23:05:20

  1. Recurrent neural networks model for WiFi-based indoor positioning system

    Yuan Lukito, Antonius Rachmat Chrismanto. 2017 International Conference on Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS), 2017. doi: 10.1109/ICON-SONICS.2017.8267833