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<p>The Internet of things (IoT) can impact healthcare by merging the most significant technology and scientific developments in the domains of automation, mobility, and data analytics to enhance patient care. Smart healthcare systems play an important role in providing patients with effective healthcare services. The requirements of patients change with time, requiring more reliable resource allocation and management. Effective resource allocation and management systems must be adaptable to meet user demands arising from the high volume of data generated in HIoT environments. There is a need for resource allocation and management mechanisms with the maximum availability of fog environments in IoT-based systems. The resources are scheduled and managed based on execution cost, network usage, latency, execution time, and energy consumption. Therefore, to address challenges regarding resource allocation, data management, and fault tolerance, we propose a multi-environment-based two-way fault-tolerant scheme for resource allocation and data management (MTF-RD). We perform simulations with a dataset on cardiovascular health monitoring (CHM) in iFogSim2. The proposed strategy is contrasted with the benchmark SMP (scalable micro-services placement)-clustering and two-phase fault-tolerant (TPFT) strategies. Comparisons are based on measures of performance including energy consumption, latency, execution time, network usage, and execution time. The cardiovascular health monitoring (CHM) application is simulated, and latency is used for electrocardiogram (ECG) monitoring. The results show that the proposed MTF-RD strategy reduces the execution cost by 11.59%, network usage by 12.51%, latency by 5.02%, execution time by 23.34%, and energy consumption by 1.71% as compared with SMP-clustering. The proposed MTF-RD strategy also&#xa0;compared with TPFT scheme and reduces the execution cost by 2.67%, network usage by 2.41%, latency by 1.65%, execution time by 7.07%, and energy consumption by 0.55%. The proposed MTF-RD has the potential to provide a multi-environment-based two-way fault-tolerant mechanism for resource allocation and data management with the integration of fog environments for healthcare IoT.</p>

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A multi-environment-based two-way fault-tolerant scheme for resource allocation and data management in healthcare Internet of things

  • 期刊论文

  • 发表日期：

    2025年11月28日

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  • Waqar Saeed,
  • Taj Rahman,
  • Asim Zeb,
  • Zulfiqar Ahmad,
  • Abdulmohsen Algarni

摘要

The Internet of things (IoT) can impact healthcare by merging the most significant technology and scientific developments in the domains of automation, mobility, and data analytics to enhance patient care. Smart healthcare systems play an important role in providing patients with effective healthcare services. The requirements of patients change with time, requiring more reliable resource allocation and management. Effective resource allocation and management systems must be adaptable to meet user demands arising from the high volume of data generated in HIoT environments. There is a need for resource allocation and management mechanisms with the maximum availability of fog environments in IoT-based systems. The resources are scheduled and managed based on execution cost, network usage, latency, execution time, and energy consumption. Therefore, to address challenges regarding resource allocation, data management, and fault tolerance, we propose a multi-environment-based two-way fault-tolerant scheme for resource allocation and data management (MTF-RD). We perform simulations with a dataset on cardiovascular health monitoring (CHM) in iFogSim2. The proposed strategy is contrasted with the benchmark SMP (scalable micro-services placement)-clustering and two-phase fault-tolerant (TPFT) strategies. Comparisons are based on measures of performance including energy consumption, latency, execution time, network usage, and execution time. The cardiovascular health monitoring (CHM) application is simulated, and latency is used for electrocardiogram (ECG) monitoring. The results show that the proposed MTF-RD strategy reduces the execution cost by 11.59%, network usage by 12.51%, latency by 5.02%, execution time by 23.34%, and energy consumption by 1.71% as compared with SMP-clustering. The proposed MTF-RD strategy also compared with TPFT scheme and reduces the execution cost by 2.67%, network usage by 2.41%, latency by 1.65%, execution time by 7.07%, and energy consumption by 0.55%. The proposed MTF-RD has the potential to provide a multi-environment-based two-way fault-tolerant mechanism for resource allocation and data management with the integration of fog environments for healthcare IoT.