Journal of Ubiquitous Computing and Communication Technologies
IRO Journals
Volume 3 — Issue 1 — March 2021

Wearable IoT based Distributed Framework for Ubiquitous Computing

https://doi.org/10.36548/jucct.2021.1.003
V. Suma
Professor, Department of Information Science & Engineering, Dayananda Sagar College of Engineering, Bangalore, India
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Suma, V. 2021. “Wearable IoT Based Distributed Framework for Ubiquitous Computing”. Journal of Ubiquitous Computing and Communication Technologies 3 (1): 23-32. https://doi.org/10.36548/jucct.2021.1.003.
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Keywords

— Storage management
— scalable network
— wearable IoT
— ubiquitous computing
— medical sensors
Published: 03-05-2021

Abstract

In Internet of Things (IoT) based systems, the multi-level user requirements are satisfied by the integration of communication technology with distributed homogeneous networks termed as the ubiquitous computing systems (UCS). The PCS demands openness in heterogeneity support, management levels and communication for distributed users. However, providing these features is still a major challenge. In wearable IoT (WIoT) based medical sensors based applications, the end users reliability of communication is enhanced using a scalable distributed computational framework introduced in this paper. The demand and sharing parameters forms the basis of analysis of resource allocation by means of recurrent learning in this framework. The rate of communication may be improved while reducing the time delay for the end users of WIoT based medical sensors with the help of UCS and estimated resource requirements. Other than data transfer, sharing and resource allocation, end-user mobility management may also be performed on the WIoT medical sensors using the proposed framework. Certain metrics are used for proving the consistency of the framework that are assessed with the help of experimental analysis and performance estimation. Parameters inclusive of storage utilization, bandwidth, request backlogs, requests handled, request failure and response time are estimated. Reduced response time, backlogs and request failure with improved storage utilization, bandwidth and requests handled are evident using the proposed framework when compared to the existing models.

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