Open AccessFog Managed Data Model for IoT based Healthcare Systems
Author(s) -
Benila S Benila S,
Usha Bhanu N. Benila S
Publication year - 2022
Publication title -
wǎngjì wǎnglù jìshù xuékān
Language(s) - English
Resource type - Journals
eISSN - 2079-4029
pISSN - 1607-9264
DOI - 10.53106/160792642022032302003
Subject(s) - computer science , cloud computing , latency (audio) , fog computing , scheduling (production processes) , edge computing , energy consumption , response time , distributed computing , edge device , real time computing , computer network , operating system , telecommunications , ecology , operations management , economics , biology
In Internet of things enabled healthcare system, sensors create vast volumes of data that are analyzed in the cloud. Transferring data from the cloud to the application takes a long time. An effective infrastructure can reduce latency and costs by processing data in real-time and close to the user devices. Fog computing can solve this issue by reducing latency by storing, processing, and analyzing patient data at the network edge. Placing the resources at fog layer and scheduling tasks is quite challenging in Fog computing. This paper proposes a Fog Managed Data Model (FMDM) with three layers namely Sensor, Fog and cloud to solve the aforementioned issue. Sensors generate patient data and that are managed and processed by Fog and cloud layers. Tasks are scheduled using a Weighted Fog Priority Job Scheduling algorithm (WFPJS) and fog nodes are allocated based on Priority based Virtual Machine Classification Algorithm (PVCA). The performance of this model is validated with static scheduling techniques with variable patient counts and network configurations. The proposed FMDM with WFPJS reduces response time, total execution cost, network usage, network latency, computational latency and energy consumption.