Performance evaluation of RSS fingerprinting method to track ZigBee devices location using artificial neural networks

Hening Pram Pradityo, Lukman Rosyidi, Misbahuddin, Riri Fitri Sari

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

One of many important activities in the Wireless Sensor Network is the localization for tracked devices. Received Signal Strength (RSS) is a parameter of the power level that being received by the radio which can be used to track the location of the devices. This paper evaluates the localization of ZigBee devices which uses RSS fingerprinting by artificial neural networks. The RSS data processing by neural networks uses two types of training algorithms, i.e. Levenberg-Marquardt algorithm and Resilient Backpropagation algorithm. The performance of these two algorithms is evaluated. The experiment result shows that the RSS fingerprinting method is able to accurately predict the location of tracked ZigBee devices. The Levenberg-Marquardt algorithm has accuracy 96.41%, which is slightly better than Resilient Backpropagation algorithm with 94.52% accuracy.

Original languageEnglish
Title of host publicationInternational Conference on Information and Communication Technology Convergence
Subtitle of host publicationICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages268-273
Number of pages6
ISBN (Electronic)9781509040315
DOIs
Publication statusPublished - 12 Dec 2017
Event8th International Conference on Information and Communication Technology Convergence, ICTC 2017 - Jeju Island, Korea, Republic of
Duration: 18 Oct 201720 Oct 2017

Publication series

NameInternational Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
Volume2017-December

Conference

Conference8th International Conference on Information and Communication Technology Convergence, ICTC 2017
Country/TerritoryKorea, Republic of
CityJeju Island
Period18/10/1720/10/17

Keywords

  • Artificial Neural Networks
  • fingerprinting
  • RSS
  • WSN
  • ZigBee

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