Abstract
In this paper, an integrated biometric-based security framework is proposed for wireless body area networks, which takes advantage of biometric features shared by body sensors deployed at different positions of a person's body. The data communications among these sensors are secured via the proposed authentication and selective encryption schemes that require low computational power and less resources (e. g., battery and bandwidth). Specifically, a wavelet-domain Hidden Markov Model (HMM) classification method is utilized for accurate authentication based on the non-Gaussian statistics of ECG (electrocardiogram) signals. In addition, the biometric information such as ECG signal is used as the biometric key for the encryption in the framework. Our experimental results demonstrate that the proposed approach can achieve accurate authentication performance without extra requirements of key distribution and strict time synchronization.