Breathing-based authentication on resource-constrained IoT devices using recurrent neural networks

Breathing-based authentication on resource-constrained IoT devices using recurrent neural networks

Recurrent neural networks (RNNs) have shown promising resultsin audio and speech-processing applications. The increasingpopularity of Internet of Things (IoT) devices makes a strongcase for implementing RNN-based inferences for applicationssuch as acoustics-based authentication and voice commandsfor...

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Bibliographic Details
Main Authors: CHAUHAN, Jagmohan, SENEVIRATNE, Suranga, HU, Yining, MISRA, Archan, SENEVIRATNE, Aruna, LEE, Youngki
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2018
Subjects:
Digital Communications and Networking
OS and Networks
Online Access:https://ink.library.smu.edu.sg/sis_research/4054
https://ink.library.smu.edu.sg/context/sis_research/article/5057/viewcontent/08364655__1_.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:Recurrent neural networks (RNNs) have shown promising resultsin audio and speech-processing applications. The increasingpopularity of Internet of Things (IoT) devices makes a strongcase for implementing RNN-based inferences for applicationssuch as acoustics-based authentication and voice commandsfor smart homes. However, the feasibility and performance ofthese inferences on resource-constrained devices remain largelyunexplored. The authors compare traditional machine-learningmodels with deep-learning RNN models for an end-to-endauthentication system based on breathing acoustics.

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