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Efficient Key Agreement Algorithm for Wireless Body Area Networks Using Reusable ECG-Based Features

Author name : OSAMA MAHMOUD OUDA
Publication Date : 2021-02-01
Journal Name : Multidisciplinary Digital Publishing Institute (MDBI)

Abstract

Wireless Body Area Networks (WBANs) are increasingly employed in different medical
applications, such as remote health monitoring, early detection of medical conditions, and computerassisted
rehabilitation. A WBAN connects a number of sensor nodes implanted in and/or fixed on
the human body for monitoring his/her physiological characteristics. Although medical healthcare
systems could significantly benefit from the advancement of WBAN technology, collecting and
transmitting private physiological data in such an open environment raises serious security and
privacy concerns. In this paper, we propose a novel key-agreement protocol to secure communications
among sensor nodes of WBANs. The proposed protocol is based on measuring and verifying common
physiological features at both sender and recipient sensors prior to communicating. Unlike existing
protocols, the proposed protocol enables communicating sensors to use their previous session preknowledge
for secure communication within a specific period of time. This will reduce the time
required for establishing the shared key as well as avoid retransmitting extracted features in the
medium and hence thwarting eavesdropping attacks while maintaining randomness of the key.
Experimental results illustrate the superiority of the proposed key agreement protocol in terms of
both feature extraction and key agreement phases with an accuracy of 99.50% and an error rate of
0.005%. The efficacy of the proposed protocol with respect to energy and memory utilization is
demonstrated compared with existing key agreement protocols.

Keywords

key agreement; Wireless Body Area Network (WBAN); inter-sensor communication; physiological feature-based key agreement; Electrical Cardio Graph (ECG); secure communication; resource saving algorithm; pattern recognition

Publication Link

https://www.mdpi.com/2079-9292/10/4/404

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