A4 Refereed article in a conference publication
Fault Tolerant and Scalable IoT-based Architecture for Health Monitoring
Authors: Gia TN, Rahmani AM, Westerlund T, Liljeberg P, Tenhunen H
Editors: No editors available
Conference name: IEEE Sensors Applications Symposium
Publication year: 2015
Book title : 2015 IEEE Sensors Applications Symposium (SAS)
Journal name in source: 2015 IEEE SENSORS APPLICATIONS SYMPOSIUM (SAS)
First page : 334
Last page: 339
Number of pages: 6
ISBN: 978-1-4799-6118-4
eISBN: 978-1-4799-6117-7
DOI: https://doi.org/10.1109/SAS.2015.7133626
Web address : http://ieeexplore.ieee.org/document/7133626/
Abstract
A novel Internet of Things based architecture supporting scalability and fault tolerance for healthcare is presented in this paper. The wireless system is constructed on top of 6LoWPAN energy efficient communication infrastructure to maximize the operation time. Fault tolerance is achieved via backup routing between nodes and advanced service mechanisms to maintain connectivity in case of failing connections between system nodes. The presented fault tolerance approach covers many fault situations such as malfunction of sink node hardware and traffic bottleneck at a node due to a high receiving data rate. A method for extending the number of medical sensing nodes at a single gateway is presented. A complete system architecture providing a quantity of features from bio- signal acquisition such as Electrocardiogram (ECG), Electroencephalography (EEG), and Electromyography (EMG) to the representation of graphical way efoi ms of these gathered bio-signals for remote real-time monitoring is proposed.
A novel Internet of Things based architecture supporting scalability and fault tolerance for healthcare is presented in this paper. The wireless system is constructed on top of 6LoWPAN energy efficient communication infrastructure to maximize the operation time. Fault tolerance is achieved via backup routing between nodes and advanced service mechanisms to maintain connectivity in case of failing connections between system nodes. The presented fault tolerance approach covers many fault situations such as malfunction of sink node hardware and traffic bottleneck at a node due to a high receiving data rate. A method for extending the number of medical sensing nodes at a single gateway is presented. A complete system architecture providing a quantity of features from bio- signal acquisition such as Electrocardiogram (ECG), Electroencephalography (EEG), and Electromyography (EMG) to the representation of graphical way efoi ms of these gathered bio-signals for remote real-time monitoring is proposed.
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