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Efficient data transmission on wireless communication through a privacy-enhanced blockchain process.

Rajanikanth Aluvalu1, Senthil Kumaran V N2, Manikandan Thirumalaisamy3

  • 1Department of IT, Chaitanya Bharathi Institute of Technology, Hyderabad, India.

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Summary
This summary is machine-generated.

This study introduces advanced wireless communication and blockchain for wearable health devices, enhancing data sensitivity and addressing privacy concerns. The proposed methods achieve high data classification accuracy, improving patient health monitoring and security.

Keywords:
BlockchainData managementGradient boostingHybrid microwave transmissionWireless technology

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Area of Science:

  • Biomedical Engineering
  • Wireless Communication
  • Data Science

Background:

  • Wearable devices collect vital health data (heart rate, ECG, SpO2, sleep, activity) via sensors, but face challenges in time sensitivity and data accuracy.
  • Existing passive data collection methods in healthcare lack efficiency and raise privacy concerns.
  • Emerging wireless communication trends and blockchain technology offer potential solutions for secure and efficient data management in healthcare.

Purpose of the Study:

  • To review and propose advanced wireless communication and blockchain strategies for enhancing wearable healthcare data management.
  • To address the limitations of time sensitivity and data accuracy in current wearable sensor systems.
  • To improve the security and privacy of patient health data collected through wearable technology.

Main Methods:

  • Review of the latest wireless communication trends in hospital wearable technology.
  • Integration of blockchain for enhanced data privacy and security.
  • Application of gradient boosting and hybrid microwave transmission for data analysis and location tracking.
  • Statistical modeling and exploratory data analysis for data classification.

Main Results:

  • The proposed gradient boosting and hybrid microwave transmission methods were applied to patient health decisions.
  • A data classification accuracy of 98% was achieved after removing unwanted data.
  • The system demonstrated effective data analysis for decision-making and improved data classification outcomes.

Conclusions:

  • Advanced wireless communication and blockchain integration significantly enhance the sensitivity and security of wearable health data.
  • The proposed methods provide a robust framework for accurate patient health monitoring and data management.
  • High data classification accuracy supports improved decision-making processes in healthcare settings.