A Fault Tolerant Data Management Scheme for Healthcare Internet of Things in Fog Computing

Fog computing aims to provide the solution of bandwidth, network latency and energy consumption problems of cloud computing Likewise, management of data generated by healthcare IoT devices is one of the significant applications of fog computing Huge amount of data is being generated by healthcare IoT devices and such types of data is required to be managed efficiently, with low latency, without failure, and with minimum energy consumption and low cost Failures of task or node can cause more latency, maximum energy consumption and high cost Thus, a failure free, cost efficient, and energy aware management and scheduling scheme for data generated by healthcare IoT devices not only improves the performance of the system but also saves the precious lives of patients because of due to minimum latency and provision of fault tolerance Therefore, to address all such challenges with regard to data management and fault tolerance, we have presented a Fault Tolerant Data management (FTDM) scheme for healthcare IoT in fog computing In FTDM, the data generated by healthcare IoT devices is efficiently organized and managed through well-defined components and steps A two way fault-tolerant mechanism i e , task-based fault-tolerance and node-based fault-tolerance, is provided in FTDM through which failure of tasks and nodes are managed The paper considers energy consumption, execution cost, network usage, latency, and execution time as performance evaluation parameters The simulation results show significantly improvements which are performed using iFogSim Further, the simulation results show that the proposed FTDM strategy reduces energy consumption 3 97%, execution cost 5 09%, network usage 25 88%, latency 44 15% and execution time 48 89% as compared with existing Greedy Knapsack Scheduling (GKS) strategy Moreover, it is worthwhile to mention that sometimes the patients are required to be treated remotely due to non-availability of facilities or due to some infectious diseases such as COVID-19 Thus, in such circumstances, the proposed strategy is significantly efficient © 2021 Korean Society for Internet Information All rights reserved