1-RAAP: An Efficient 1-Round Anonymous Authentication Protocol for Wireless Body Area Networks

Thanks to the rapid technological convergence of wireless communications, medical sensors and cloud computing, Wireless Body Area Networks (WBANs) have emerged as a novel networking paradigm enabling ubiquitous Internet services, allowing people to receive medical care, monitor health status in real...

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Bibliographic Details
Main Authors: Jingwei Liu, Lihuan Zhang, Rong Sun
Format: Article
Language:English
Published: MDPI AG 2016-05-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/16/5/728
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Summary:Thanks to the rapid technological convergence of wireless communications, medical sensors and cloud computing, Wireless Body Area Networks (WBANs) have emerged as a novel networking paradigm enabling ubiquitous Internet services, allowing people to receive medical care, monitor health status in real-time, analyze sports data and even enjoy online entertainment remotely. However, because of the mobility and openness of wireless communications, WBANs are inevitably exposed to a large set of potential attacks, significantly undermining their utility and impeding their widespread deployment. To prevent attackers from threatening legitimate WBAN users or abusing WBAN services, an efficient and secure authentication protocol termed 1-Round Anonymous Authentication Protocol (1-RAAP) is proposed in this paper. In particular, 1-RAAP preserves anonymity, mutual authentication, non-repudiation and some other desirable security properties, while only requiring users to perform several low cost computational operations. More importantly, 1-RAAP is provably secure thanks to its design basis, which is resistant to the anonymous in the random oracle model. To validate the computational efficiency of 1-RAAP, a set of comprehensive comparative studies between 1-RAAP and other authentication protocols is conducted, and the results clearly show that 1-RAAP achieves the best performance in terms of computational overhead.
ISSN:1424-8220