Redemptive Resource Sharing and Allocation Scheme for Internet of Things-Assisted Smart Healthcare Systems

Area of Science:

• Healthcare Technology
• Computer Science
• Network Engineering

Background:

• Internet of Things (IoT) enables advanced healthcare services through heterogeneous communication.
• Current IoT healthcare systems face challenges with resource sharing and diagnosis speed due to proactive sharing and delayed transmissions.
• Inefficient resource allocation and data transmission hinder the reliability and swiftness of IoT-assisted clinical diagnosis.

Purpose of the Study:

• To introduce and evaluate the Redemptive Resource Sharing and Allocation (R2SA) scheme for IoT-assisted healthcare.
• To address the complications of resource sharing and diagnosis swiftness in IoT healthcare environments.
• To improve data accumulation ratios, reduce transmission delays, and enhance resource allocation efficiency.

Main Methods:

• Implemented a first-come, first-serve data accumulation approach with infrastructure selection.
• Introduced the R2SA scheme for non-redemptive and parallel resource allocation based on data-to-capacity analysis.
• Utilized transfer learning, data-to-capacity validation, and concurrent recommendation for process management.

Main Results:

• The R2SA scheme effectively reduces transmission delay and complexity in IoT healthcare systems.
• Concurrent redemptive selection and sharing of resources lead to improved resource allocation.
• The system demonstrates a better data accumulation ratio through concurrent sharing and allocation processes.

Conclusions:

• The R2SA scheme offers a robust solution for optimizing resource management in IoT-assisted healthcare.
• Efficient resource allocation and reduced transmission delays contribute to more reliable and faster clinical diagnosis.
• The proposed method enhances the overall performance and scalability of IoT healthcare services.