Towards a Smart Fault Tolerant Indoor Localization System through Recurrent Neural Networks

Eduardo C. Carvalho, Bruno V. Ferreira, Geraldo P. R. Filho, Pedro H. Gomes, Gustavo M. Freitas, Patricia A. Vargas, Jó Ueyama, Gustavo Pessin

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

1 Citation (Scopus)
41 Downloads (Pure)

Abstract

This paper proposes a fault-tolerant indoor localization system that employs Recurrent Neural Networks (RNNs) for the localization task. A decision module is designed to detect failures and this is responsible for the allocation of RNNs that are suitable for each situation. As well as the fault-tolerant system, several architectures and models for RNNs are exploited in the system: Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM) and Simple RNN. The system uses as inputs a collection of Wi-Fi Received Signal Strength Indication (RSSI) signals, and the RNN classifies the position of an agent on the basis of this collection. A fault-tolerant mechanism has been designed to handle two types of failures: (i) momentary failure, and (ii) permanent failure. The results show that the RNNs are suitable for tackling the problem and that the whole system is reliable when employed for a series of failures.

Original languageEnglish
Title of host publication2019 International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
ISBN (Electronic)9781728119854
DOIs
Publication statusPublished - 30 Sep 2019
Event2019 International Joint Conference on Neural Network - Budapest, Hungary
Duration: 14 Jul 201919 Jul 2019

Publication series

NameInternational Joint Conference on Neural Networks
PublisherIEEE
ISSN (Electronic)2161-4407

Conference

Conference2019 International Joint Conference on Neural Network
Abbreviated title IJCNN 2019
CountryHungary
CityBudapest
Period14/07/1919/07/19

Keywords

  • Fault-Tolerance
  • Gated Recurrent Unit
  • Indoor Localization
  • Intelligent Control
  • Long Short-Term Memory

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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