An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks.

Area of Science:

• Biomedical Engineering
• Cybersecurity
• Signal Processing

Background:

• Body Sensor Networks (BSN) collect vital physiological data (EEG, PPG, ECG) requiring robust security.
• Current BSN security methods rely on complex cryptography, leading to high resource and energy consumption.
• There is a critical need for energy-efficient and computationally simple authentication for BSN.

Purpose of the Study:

• To propose a novel biometric-based algorithm for securing Body Sensor Networks (BSN).
• To utilize Heart Rate Variability (HRV) for a simplified key generation process in BSN security.
• To evaluate the efficiency of the proposed algorithm against existing methods.

Main Methods:

• Developed a new algorithm leveraging Heart Rate Variability (HRV) for biometric-based key generation.
• Compared the proposed algorithm with Physiological Signal based Key Agreement (PSKA), Data Encryption Standard (DES), and Rivest Shamir Adleman (RSA).
• Conducted simulations using Matlab to assess performance metrics.

Main Results:

• The proposed HRV-based algorithm demonstrated superior efficiency in terms of transmission time.
• The algorithm showed better performance in average remaining energy compared to existing methods.
• Lower total power consumption was observed with the novel approach.

Conclusions:

• The proposed biometric-based algorithm provides an energy-efficient and computationally less complex solution for BSN security.
• HRV is an effective biometric parameter for secure and efficient key generation in BSN.
• The findings suggest a viable alternative to resource-intensive cryptographic methods for BSN authentication.