Heartbeats Based Biometric Random Binary Sequences Generation to Secure Wireless Body Sensor Networks.

Area of Science:

• Biometrics and Signal Processing
• Cybersecurity in Healthcare
• Wireless Body Sensor Networks (WBSNs)

Background:

• Heartbeat-based random binary sequences (RBSs) are vital for WBSN security.
• Existing methods for generating RBSs from heartbeats are time-consuming (∼25-30 s for 128 bits).
• Real-time healthcare applications require more efficient RBS generation techniques.

Purpose of the Study:

• To develop a novel, time-efficient biometric technique for generating RBSs using heartbeats.
• To improve the speed of RBS generation for secure WBSN applications.
• To extract entropic bits from interpulse intervals (IPIs) for enhanced security.

Main Methods:

• A new technique using interpulse intervals (IPIs) of heartbeats for biometric RBS generation.
• Incorporation of a finite monotonic increasing sequences generation mechanism for IPIs.
• A cyclic block encoding procedure to extract entropic bits from each IPI.
• Validation using 89 ECG recordings from healthy individuals, a standard database, and cardiac patients.

Main Results:

• The proposed technique can extract up to 16 random bits per heartbeat, generating 128-bit RBSs from eight consecutive IPIs.
• Generated RBSs demonstrate high randomness and distinctiveness, validated by NIST statistical tests and Hamming distance.
• The approach is up to four times faster than existing heartbeat-based RBS generation schemes, requiring only 0-8 seconds.
• Generated RBSs are suitable for encryption keys or entity identifiers in WBSNs.

Conclusions:

• The developed IPI-based technique offers a significantly faster and efficient method for generating secure RBSs.
• This advancement is crucial for real-time health monitoring and securing WBSNs.
• The method provides a viable solution for enhancing security in healthcare applications without compromising performance.