@inproceedings{22c04782285c4cb3ae1f9d3abf93bfc3,
title = "Towards a Smart Fault Tolerant Indoor Localization System through Recurrent Neural Networks",
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.",
keywords = "Fault-Tolerance, Gated Recurrent Unit, Indoor Localization, Intelligent Control, Long Short-Term Memory",
author = "Carvalho, {Eduardo C.} and Ferreira, {Bruno V.} and Filho, {Geraldo P. R.} and Gomes, {Pedro H.} and Freitas, {Gustavo M.} and Vargas, {Patricia A.} and J{\'o} Ueyama and Gustavo Pessin",
year = "2019",
month = sep,
day = "30",
doi = "10.1109/IJCNN.2019.8852007",
language = "English",
series = "International Joint Conference on Neural Networks",
publisher = "IEEE",
booktitle = "2019 International Joint Conference on Neural Networks (IJCNN)",
address = "United States",
note = "2019 International Joint Conference on Neural Network, IJCNN 2019 ; Conference date: 14-07-2019 Through 19-07-2019",
}