Heartbeats based biometric random binary sequences generation to secure wireless body sensor networks

Sandeep Pirbhulal, Heye Zhang, Wanqing Wu, Subhas Chandra Mukhopadhyay, Yuan-Ting Zhang

Research output: Contribution to journalArticlepeer-review

90 Citations (Scopus)

Abstract

Heartbeats based random binary sequences (RBSs) are the backbone for several security aspects in wireless body sensor networks (WBSNs). However, current heartbeats based methods require a lot of processing time (∼25-30 s) to generate 128-bit RBSs in real-time healthcare applications. In order to improve time efficiency, a biometric RBSs generation technique using interpulse intervals (IPIs) of heartbeats is developed in this study. The proposed technique incorporates a finite monotonic increasing sequences generation mechanism of IPIs and a cyclic block encoding procedure that extracts a high number of entropic bits from each IPI. To validate the proposed technique, 89 ECG recordings including 25 healthy individuals in a laboratory environment, 20 from MIT-BIH Arrhythmia Database, and 44 cardiac patients from the clinical environment are considered. By applying the proposed technique on the ECG signals, at most 16 random bits can be extracted from each heartbeat to generate 128-bit RBSs via concatenation of eight consecutive IPIs. And the randomness and distinctiveness of generated 128-bit RBSs are measured based on the National Institute of Standards and Technology statistical tests and hamming distance, respectively. From the experimental results, the generated 128-bit RBSs from both healthy subjects and patients can potentially be used as keys for encryption or entity identifiers to secure WBSNs. Moreover, the proposed approach is examined to be up to four times faster than the existing heartbeat-based RBSs generation schemes. Therefore, the developed technique necessitates less processing time (0-8 s) in real-time health monitoring scenarios to construct 128-bit RBSs in comparisons with current methods.

Original languageEnglish
Pages (from-to)2751-2759
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Volume65
Issue number12
DOIs
Publication statusPublished - Dec 2018

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