ECG-RNG: A Random Number Generator Based on ECG Signals and Suitable for Securing Wireless Sensor Networks

Area of Science:

• Computer Science
• Electrical Engineering
• Biomedical Engineering

Background:

• Wireless Sensor Networks (WSNs) are vital for applications like environmental monitoring and healthcare but face security threats due to wireless connectivity.
• Cryptographic solutions, including Random Number Generators (RNGs), are crucial for WSN security, particularly for authentication and key generation.
• Existing RNGs may not be optimally suited for resource-constrained WSN devices.

Purpose of the Study:

• To propose and evaluate an avant-garde True Random Number Generator (TRNG) for securing Wireless Sensor Networks (WSNs).
• To leverage biological signals, specifically cardiac signals, as a source for generating random bits.
• To validate the randomness and suitability of the proposed TRNG for WSN security applications.

Main Methods:

• Utilized electrocardiogram (ECG) signals from a large public dataset (202 subjects, 24 hours).
• Applied multi-level decomposition via wavelet analysis for extracting random bits from cardiac signals.
• Assessed the TRNG's output using rigorous statistical test batteries (ENT, DIEHARDER, NIST), bias, distinctiveness, and performance analysis.

Main Results:

• The proposed TRNG, utilizing cardiac signals and wavelet decomposition, demonstrated output streams behaving as random variables.
• Statistical tests (ENT, DIEHARDER, NIST) confirmed the high quality of randomness.
• The TRNG exhibited suitable performance characteristics for WSN applications.

Conclusions:

• The proposed cardiac-signal-based TRNG is a viable and effective solution for enhancing the security of Wireless Sensor Networks.
• The method provides a novel, biologically-inspired approach to random number generation for embedded systems.
• The TRNG's proven randomness and performance make it suitable for cryptographic protocols in WSNs.