Exploiting the use of machine learning in two different sensor network architectures for indoor localization

Eduardo Carvalho, Bruno S. Faiçal, Geraldo P. R. Filho, Patricia A Vargas, Jó Ueyama, Gustavo Pessin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Indoor localization has been an active research area for the last two decades. This emerged in the context of providing a mobile robot the capability to conduct navigation tasks in indoor environments. Although the sensing technologies and techniques proposed for indoor robot localization have proven to be reliable solutions, these cannot be adopted as a solution to people or object localization for indoor environments, particularly, due to their high computational cost and power requirements. In order to mitigate these issues, a low-power consumption sensing technology, based on the strength of WiFi signals, is being studied. Nevertheless, a concern when working with these signals is their vulnerability to interference. This paper exploits the use of machine learning is two different architectures for localization and present how a data filtering technique can alleviate interferences. A step into a fault tolerance approach is also given, presenting that the system can maintain certain reliability even losing some of its parts.

Original languageEnglish
Title of host publicationProceedings of 2016 IEEE International Conference on Industrial Technology (ICIT)
PublisherIEEE
Pages652-657
Number of pages6
ISBN (Print)9781467380751
DOIs
Publication statusPublished - 2016
Event2016 IEEE International Conference on Industrial Technology - Taipei, Taiwan, Province of China
Duration: 14 Mar 201617 Mar 2016

Conference

Conference2016 IEEE International Conference on Industrial Technology
Abbreviated titleICIT 2016
CountryTaiwan, Province of China
CityTaipei
Period14/03/1617/03/16

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Exploiting the use of machine learning in two different sensor network architectures for indoor localization'. Together they form a unique fingerprint.

  • Cite this

    Carvalho, E., Faiçal, B. S., Filho, G. P. R., Vargas, P. A., Ueyama, J., & Pessin, G. (2016). Exploiting the use of machine learning in two different sensor network architectures for indoor localization. In Proceedings of 2016 IEEE International Conference on Industrial Technology (ICIT) (pp. 652-657). IEEE. https://doi.org/10.1109/ICIT.2016.7474826