L/F-CIPS: Collaborative Indoor Positioning for Smartphones With Lateration and Fingerprinting

Pavel Pascacio, Joaquín Torres-Sospedra, Sven Casteleyn, Elena Simona Lohan, Jari Nurmi

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
11 Downloads (Pure)


The demand for indoor location-based services (LBS) and the wide availability of mobile devices have triggered research into new positioning systems able to provide accurate indoor positions using smartphones. However, accurate solutions require a complex implementation and long-term maintenance of their infrastructure. Collaborative systems may help alleviate these drawbacks. In this article, we propose a smartphone-based collaborative architecture using neural networks and received signal strength (RSS), which exploits the built-in wireless communication technologies in smartphones and the collaboration between devices to improve the traditional positioning systems without additional deployment. Experiments are carried out in two real-world scenarios, demonstrating that our proposed architecture enhances the position accuracy of the traditional indoor positioning systems (IPSs).

Original languageEnglish
Pages (from-to)24787-24799
Number of pages13
JournalIEEE Sensors Journal
Issue number20
Publication statusPublished - 15 Oct 2023
Publication typeA1 Journal article-refereed


  • Collaborative indoor positioning
  • fingerprinting
  • lateration
  • neural networks
  • received signal strength

Publication forum classification

  • Publication forum level 2

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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