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Erschienen in: Cluster Computing 1/2018

08.12.2016

An IoT healthcare service model of a vehicle using implantable devices

verfasst von: Yoon-Su Jeong, Seung-Soo Shin

Erschienen in: Cluster Computing | Ausgabe 1/2018

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Abstract

As IoT technologies have become more available, healthcare patients increasingly want to be provided with services at places other than hospitals or their homes. Most patients with implantable devices still visit hospitals, sometimes using a self-driving car or public transportation to obtain services. When an emergency situation develops for a patient in a vehicle lacking the means to address the crisis, the patient’s life cannot help but be in danger. The present paper proposes an IoT healthcare service model that will enable patients with a medical sensor to be provided healthcare services in a vehicle installed with IoT devices. To solve problems in existing models that do not include electromagnetic interference-based (EMI) multiple property management and control, the proposed model involves medical sensors with different multiple-property information guarantee targeted SINRs and minimum blackouts. The model also features the ability to connect to hospital healthcare service centers using the IoT devices installed in vehicles, thereby enabling information on the patient’s condition and first aid needs to be transmitted in real time. To secure the patient’s biometric data during information transmission, the proposed model weights that information to enhance the efficiency of the IoT devices. Performance evaluation results revealed that compared to existing algorithms, the communication strength of the proposed model is an average of 5.2% higher, and network efficiency between IoT devices and medical sensors is an average of 7.6% higher. In addition, the overhead on IoT devices was an average of 3.5% lower.

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Metadaten
Titel
An IoT healthcare service model of a vehicle using implantable devices
verfasst von
Yoon-Su Jeong
Seung-Soo Shin
Publikationsdatum
08.12.2016
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe 1/2018
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-016-0689-z

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