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DRLBTS: deep reinforcement learning-aware blockchain-based healthcare system.

Abdullah Lakhan1,2,3, Mazin Abed Mohammed4,2,3, Jan Nedoma2

  • 1Department of Computer Science, Dawood University of Engineering and Technology, Sindh, Karachi, 74800, Pakistan.

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Summary
This summary is machine-generated.

This study introduces a novel deep reinforcement learning-aware blockchain-based task scheduling (DRLBTS) algorithm for Industrial Internet of Things (IIoT) healthcare applications. DRLBTS enhances security and optimizes task scheduling, ensuring efficient and private data sharing in distributed networks.

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Area of Science:

  • * Industrial Internet of Things (IIoT) applications in healthcare.
  • * Integration of advanced technologies for remote patient monitoring and data management.

Background:

  • * Current Industrial Internet of Things (IIoT) healthcare systems face challenges in security, task scheduling, and processing costs.
  • * Remote healthcare technologies utilizing biomedical sensors and wireless communication are prevalent but require robust solutions.

Purpose of the Study:

  • * To propose a new algorithm addressing security, privacy, and efficiency in IIoT-based healthcare.
  • * To enhance task scheduling and secure data sharing for distributed healthcare applications.

Main Methods:

  • * Development of a deep reinforcement learning-aware blockchain-based task scheduling (DRLBTS) framework.
  • * Implementation of secure and validated data sharing mechanisms between network nodes.
  • * Focus on makespan-efficient scheduling for healthcare tasks.

Main Results:

  • * DRLBTS demonstrates adaptive capabilities for healthcare applications.
  • * The framework effectively meets security, privacy, and makespan requirements.
  • * Statistical results validate the algorithm's performance in distributed networks.

Conclusions:

  • * The proposed DRLBTS algorithm offers a secure and efficient solution for IIoT healthcare.
  • * DRLBTS successfully balances task scheduling, data security, and privacy needs.
  • * This framework is suitable for advanced distributed healthcare systems.