A Novel Hybrid Approach-based on Heartbeat and Accelerometer Signals for Enhanced Security in WBSNs

In Wireless Body Sensor Networks (WBSNs), ensuring secure and efficient key distribution is critical, particularly given the limited computational and energy resources of the sensors. Existing methods often struggle to balance security with these resource constraints, especially in environments involving physiological data such as acceleration (ACC) and electrocardiogram (ECG) signals. For the first time, this study proposes a novel hybrid approach that integrate fuzzy commitment with ACC signal noise and ECG features for efficient key generation and distribution in WBSNs. By employing a low-pass filter to process ACC signal noise, we generated highly random binary sequences, leveraging the inherent randomness of the signal for secure key generation. Concurrently, an optimized coding scheme was built for ECG feature construction to ensure secure key distribution between devices. Extensive experiments, including entropy analysis and National Institute of Standards and Technology (NIST) statistical tests, confirm the robustness and security of our method. The proposed scheme achieves a false acceptance rate (FAR) of 5.23%, demonstrating superior performance across multiple databases in comparison to benchmark approaches. This novel dual-key generation strategy that combines the unpredictability of ACC noise with the individual-specific traits of ECG signals, can significantly enhance the security, applicability, and versatility of WBSNs.