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Wireless Communications and Mobile Computing
Volume 2018, Article ID 3403456, 11 pages
Research Article

Muscle Activity-Driven Green-Oriented Random Number Generation Mechanism to Secure WBSN Wearable Device Communications

1Jiangxi Normal University, Nanchang, China
2Department of Information Security Engineering, Soonchunhyang University, Asan, Republic of Korea
3Security Research Institute, Edith Cowan University, Perth WA 6027, Australia
4Chinese Academy of Sciences (CAS), Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, China
5Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Correspondence should be addressed to Ilsun You; moc.liamg@unusli

Received 13 April 2018; Accepted 27 June 2018; Published 19 August 2018

Academic Editor: Ding Wang

Copyright © 2018 Yuanlong Cao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Wireless body sensor networks (WBSNs) mostly consist of low-cost sensor nodes and implanted devices which generally have extremely limited capability of computations and energy capabilities. Hence, traditional security protocols and privacy enhancing technologies are not applicable to the WBSNs since their computations and cryptographic primitives are normally exceedingly complicated. Nowadays, mobile wearable and wireless muscle-computer interfaces have been integrated with the WBSN sensors for various applications such as rehabilitation, sports, entertainment, and healthcare. In this paper, we propose MGRNG, a novel muscle activity-driven green-oriented random number generation mechanism which uses the human muscle activity as green energy resource to generate random numbers (RNs). The RNs can be used to enhance the privacy of wearable device communications and secure WBSNs for rehabilitation purposes. The method was tested on 10 healthy subjects as well as 5 amputee subjects with 105 segments of simultaneously recorded surface electromyography signals from their forearm muscles. The proposed MGRNG requires only one second to generate a 128-bit RN, which is much more efficient when compared to the electrocardiography-based RN generation algorithms. Experimental results show that the RNs generated from human muscle activity signals can pass the entropy test and the NIST random test and thus can be used to secure the WBSN nodes.