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© 2020 ACM.Recently, Internet of Things (IoT) has raised as an important research area that combines the environmental sensing and machine learning capabilities to flourish the concept of smart spaces, in which intelligent and customized services can be provided to users in a smart manner. In smart spaces, one fundamental service that needs to be provided is accurate and unobtrusive user identification. In this work, to address this challenge, we propose a Gait Recognition as a Service (GRaaS) model, which is an instantiation of the traditional Sensing as a Service (S2aaS) model, and is specially deigned for user identification using gait in smart spaces. To illustrate the idea, a Radio Frequency Identification (RFID)-based gait recognition service is designed and implemented following the GRaaS concept. Novel tag selection algorithms and attention-based Long Short-term Memory (At-LSTM) models are designed to realize the device layer and edge layer, achieving a robust recognition with 96.3% accuracy. Extensive evaluations are provided, which show that the proposed service has accurate and robust performance and has great potential to support future smart space applications.
Author(s): Luo C, Wu J, Li J, Wang J, Xu W, Ming Z, Wei B, Li W, Zomaya AY
Publication type: Article
Publication status: Published
Journal: ACM Transactions on Internet of Things
Year: 2020
Volume: 1
Issue: 1
Print publication date: 01/02/2020
Online publication date: 02/03/2020
Acceptance date: 01/10/2019
ISSN (print): 2691-1914
ISSN (electronic): 2577-6207
Publisher: Association for Computing Machinery
URL: https://doi.org/10.1145/3375799
DOI: 10.1145/3375799
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