Comparative study of supervised learning and metaheuristic algorithms for the development of bluetooth-based indoor localization mechanisms

Jesus Lovon-Melgarejo, Manuel Castillo-Cara, Oscar Huarcaya-Canal, Luis Orozco-Barbosa, Ismael Garcia-Varea

Research output: Contribution to journalArticle (Contribution to Journal)peer-review

6 Scopus citations

Abstract

The development of the Internet of Things (IoT) benefits from 1) the connections between devices equipped with multiple sensors; 2) wireless networks and; 3) processing and analysis of the gathered data. The growing interest in the use of IoT technologies has led to the development of numerous diverse applications, many of which are based on the knowledge of the end user's location and profile. This paper investigates the characterization of Bluetooth signals behavior using 12 different supervised learning algorithms as a first step toward the development of fingerprint-based localization mechanisms. We then explore the use of metaheuristics to determine the best radio power transmission setting evaluated in terms of accuracy and mean error of the localization mechanism. We further tune-up the supervised algorithm hyperparameters. A comparative evaluation of the 12 supervised learning and two metaheuristics algorithms under two different system parameter settings provide valuable insights into the use and capabilities of the various algorithms on the development of indoor localization mechanisms.

Original languageEnglish
Article number8642816
Pages (from-to)26123-26135
Number of pages13
JournalIEEE Access
Volume7
DOIs
StatePublished - 2019
Externally publishedYes

Keywords

  • benchmark
  • Bluetooth
  • classification model
  • fingerprinting
  • Indoor positioning
  • metaheuristic optimization algorithms
  • multipath fading
  • received signal strength indication
  • signal processing
  • transmission power

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