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Towards better generalization in WLAN positioning systems with genetic algorithms and neural networks

Authors:

Diogo M. F. Izidio,

Antonyus P. do A. Ferreira,

Edna N. da S. BarrosAuthors Info & Claims

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference

Pages 1206 - 1213

https://doi.org/10.1145/3321707.3321712

Published: 13 July 2019 Publication History

Abstract

The most widely used positioning system today is the GPS (Global Positioning System), which has many commercial, civil and military applications, being present in most smartphones. However, this system does not perform well in indoor locations, which poses a constraint for the positioning task on environments like shopping malls, office buildings, and other public places. In this context, WLAN positioning systems based on fingerprinting have attracted a lot of attention as a promising approach for indoor localization while using the existing infrastructure. This paper contributes to this field by presenting a methodology for developing WLAN positioning systems using genetic algorithms and neural networks. The fitness function of the genetic algorithm is based on the generalization capabilities of the network for test points that are not included in the training set. By using this approach, we have achieved state-of-the-art results with few parameters, and our method has shown to be less prone to overfitting than other techniques in the literature, showing better generalization in points that are not recorded on the radio map.

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Cited By

Bellavista-Parent VTorres-Sospedra JPérez-Navarro A(2022)Comprehensive Analysis of Applied Machine Learning in Indoor Positioning Based on Wi-Fi: An Extended Systematic ReviewSensors10.3390/s2212462222:12(4622)Online publication date: 19-Jun-2022
https://doi.org/10.3390/s22124622
Bellavista-Parent VTorres-Sospedra JPerez-Navarro A(2021)New trends in indoor positioning based on WiFi and machine learning: A systematic review2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN)10.1109/IPIN51156.2021.9662521(1-8)Online publication date: 29-Nov-2021
https://doi.org/10.1109/IPIN51156.2021.9662521
Roy PChowdhury C(2021)A Survey of Machine Learning Techniques for Indoor Localization and Navigation SystemsJournal of Intelligent & Robotic Systems10.1007/s10846-021-01327-z101:3Online publication date: 4-Mar-2021
https://doi.org/10.1007/s10846-021-01327-z

Index Terms

Towards better generalization in WLAN positioning systems with genetic algorithms and neural networks
1. Networks
  1. Network services
    1. Location based services
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques

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Published In

cover image ACM Conferences

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference

July 2019

1545 pages

ISBN:9781450361118

DOI:10.1145/3321707

Editor:
Manuel López-Ibáñez
University of Manchester, UK
,
General Chairs:
Anne Auger
Inria and Ecole Polytechnique, France
,
Thomas Stützle
IRIDIA, Université libre de Bruxelles Belgium

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2019

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Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco

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GECCO '19

Sponsor:

SIGEVO

GECCO '19: Genetic and Evolutionary Computation Conference

July 13 - 17, 2019

Prague, Czech Republic

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Bellavista-Parent VTorres-Sospedra JPérez-Navarro A(2022)Comprehensive Analysis of Applied Machine Learning in Indoor Positioning Based on Wi-Fi: An Extended Systematic ReviewSensors10.3390/s2212462222:12(4622)Online publication date: 19-Jun-2022
https://doi.org/10.3390/s22124622
Bellavista-Parent VTorres-Sospedra JPerez-Navarro A(2021)New trends in indoor positioning based on WiFi and machine learning: A systematic review2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN)10.1109/IPIN51156.2021.9662521(1-8)Online publication date: 29-Nov-2021
https://doi.org/10.1109/IPIN51156.2021.9662521
Roy PChowdhury C(2021)A Survey of Machine Learning Techniques for Indoor Localization and Navigation SystemsJournal of Intelligent & Robotic Systems10.1007/s10846-021-01327-z101:3Online publication date: 4-Mar-2021
https://doi.org/10.1007/s10846-021-01327-z

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