An Efficient Clustering Technique for Wireless Body Area Networks Based on Dragonfly Optimization

2021 ◽  
pp. 27-42
Author(s):  
Bilal Mehmood ◽  
Farhan Aadil
Keyword(s):  
Body Area Networks ◽  
Wireless Body Area Networks ◽  
Body Area ◽  
Clustering Technique

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

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 404
Author(s):  
Yasmeen Al-Saeed ◽  
Eman Eldaydamony ◽  
Ahmed Atwan ◽  
Mohammed Elmogy ◽  
Osama Ouda
Keyword(s):  
Secure Communication ◽  
Key Agreement ◽  
Body Area Networks ◽  
Wireless Body Area Networks ◽  
Sensor Nodes ◽  
Security And Privacy ◽  
Physiological Data ◽  
Computer Assisted ◽  
Body Area ◽  
Key Agreement Protocol

Wireless Body Area Networks (WBANs) are increasingly employed in different medical applications, such as remote health monitoring, early detection of medical conditions, and computer-assisted 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 pre-knowledge 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.

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