Title :
Analysis of Using Interpulse Intervals to Generate 128-Bit Biometric Random Binary Sequences for Securing Wireless Body Sensor Networks
Author :
Zhang, Guang-He ; Poon, Carmen C Y ; Zhang, Yuan-Ting
Author_Institution :
Inst. of Comput. Technol., Grad. Univ. of CAS, Beijing, China
Abstract :
Wireless body sensor network (WBSN), a key building block for m-Health, demands extremely stringent resource constraints and thus lightweight security methods are preferred. To minimize resource consumption, utilizing information already available to a WBSN, particularly common to different sensor nodes of a WBSN, for security purposes becomes an attractive solution. In this paper, we tested the randomness and distinctiveness of the 128-bit biometric binary sequences (BSs) generated from interpulse intervals (IPIs) of 20 healthy subjects as well as 30 patients suffered from myocardial infarction and 34 subjects with other cardiovascular diseases. The encoding time of a biometric BS on a WBSN node is on average 23 ms and memory occupation is 204 bytes for any given IPI sequence. The results from five U.S. National Institute of Standards and Technology statistical tests suggest that random biometric BSs can be generated from both healthy subjects and cardiovascular patients and can potentially be used as authentication identifiers for securing WBSNs. Ultimately, it is preferred that these biometric BSs can be used as encryption keys such that key distribution over the WBSN can be avoided.
Keywords :
binary sequences; body sensor networks; encoding; statistical testing; telecommunication security; authentication identifiers; biometric random binary sequences; cardiovascular diseases; encoding time; encryption keys; m-health; myocardial infarction; securing wireless body sensor networks; statistical tests; word length 128 bit; Electrocardiography; Encoding; Entropy; Myocardium; NIST; Random sequences; Security; Biometrics; body sensor networks; cardiovascular health informatics; random number generation; security; wearable sensors; Adult; Aged; Aged, 80 and over; Cardiovascular Diseases; Case-Control Studies; Clothing; Female; Humans; Male; Middle Aged; Remote Sensing Technology; Signal Processing, Computer-Assisted;
Journal_Title :
Information Technology in Biomedicine, IEEE Transactions on
DOI :
10.1109/TITB.2011.2173946