Scaled conjugate gradient neural network for optimizing indoor positioning system

Nour Aburaed, Shadi Atalla, Husameldin Mukhtar, Mina Al-Saad, Wathiq Mansoor

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

4 Citations (Scopus)

Abstract

In this paper, several indoor positioning systems are reviewed and a deep neural network (DNN) algorithm based on Scaled Conjugate Gradient (SCG) algorithm is proposed. In the proposed indoor positioning system, Received Signal Strength (RSS) is used as a fingerprint to identify the indoor location in terms of Building and Floor. The performance of the system is evaluated and compared against other machine learning based positioning systems. The accuracy of the proposed DNN is 99% when tested using a standard dataset.

Original languageEnglish
Title of host publication2019 International Symposium on Networks, Computers and Communications, ISNCC 2019
Place of PublicationPiscataway, N.J.
PublisherIEEE
Number of pages4
ISBN (Electronic)9781728112435
DOIs
Publication statusPublished - 21 Nov 2019
Event2019 International Symposium on Networks, Computers and Communications, ISNCC 2019 - Istanbul, Turkey
Duration: 18 Jun 201920 Jun 2019

Publication series

Name2019 International Symposium on Networks, Computers and Communications, ISNCC 2019

Conference

Conference2019 International Symposium on Networks, Computers and Communications, ISNCC 2019
Country/TerritoryTurkey
CityIstanbul
Period18/06/1920/06/19

Keywords

  • classification
  • deep neural networks
  • indoor positioning system
  • optimization
  • RSS
  • scaled conjugate gradient

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