Evidence map›Paper›PMID 27213384›Full record

ArticleSensors (Basel, Switzerland)2016

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

Jingwei Liu, Lihuan Zhang, Rong Sun

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jingwei LiuState Key Laboratory of Integrated Services Networks, Xidian University, No.2, South Taibai Road, Xi'an, Shaanxi 710071, China. jwliu@mail.xidian.edu.cn.
Lihuan ZhangState Key Laboratory of Integrated Services Networks, Xidian University, No.2, South Taibai Road, Xi'an, Shaanxi 710071, China. zhanglihuan678@163.com.
Rong SunState Key Laboratory of Integrated Services Networks, Xidian University, No.2, South Taibai Road, Xi'an, Shaanxi 710071, China. rsun@mail.xidian.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

1-RAAPanonymityauthentication protocolsecuritysensorswireless body area networks

Identifiers

PMID27213384
PMCPMC4883419

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.