Evidence map›Paper›PMID 30134589›Full record

ArticleSensors (Basel, Switzerland)2018

ECG-RNG: A Random Number Generator Based on ECG Signals and Suitable for Securing Wireless Sensor Networks.

Carmen Camara, Pedro Peris-Lopez, Honorio Martín, Mu'awya Aldalaien

Abstract readEvaluation Study
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Carmen CamaraDepartment of Computer Science, University Carlos III of Madrid, 28911 Leganés, Spain. macamara@pa.uc3m.es.
Pedro Peris-LopezDepartment of Computer Science, University Carlos III of Madrid, 28911 Leganés, Spain. pperis@inf.uc3m.es.ORCID 0000-0001-6943-0760
Honorio MartínDepartment of Electronic Technology, University Carlos III of Madrid, 28911 Leganés, Spain. hmartin@ing.uc3m.es.ORCID 0000-0002-8720-406X
Mu'awya AldalaienHigher Colleges of Technology, Abu Dhabi Women's College, Abu Dhabi 41012, United Arab Emirates. maldalaien@hct.ac.ae.ORCID 0000-0002-1407-652X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wireless Sensor Networks (WSNs) are a promising technology with applications in many areas such as environment monitoring, agriculture, the military field or health-care, to name but a few. Unfortunately, the wireless connectivity of the sensors opens doors to many security threats, and therefore, cryptographic solutions must be included on-board these devices and preferably in their design phase. In this vein, Random Number Generators (RNGs) play a critical role in security solutions such as authentication protocols or key-generation algorithms. In this article is proposed an avant-garde proposal based on the cardiac signal generator we carry with us (our heart), which can be recorded with medical or even low-cost sensors with wireless connectivity. In particular, for the extraction of random bits, a multi-level decomposition has been performed by wavelet analysis. The proposal has been tested with one of the largest and most publicly available datasets of electrocardiogram signals (202 subjects and 24 h of recording time). Regarding the assessment, the proposed True Random Number Generator (TRNG) has been tested with the most demanding batteries of statistical tests (ENT, DIEHARDERand NIST), and this has been completed with a bias, distinctiveness and performance analysis. From the analysis conducted, it can be concluded that the output stream of our proposed TRNG behaves as a random variable and is suitable for securing WSNs.

Indexed as

ElectrocardiographyWireless TechnologyAlgorithmsDatasets as TopicFemaleHumansMaleMonitoring, PhysiologicRandom AllocationWavelet AnalysisElectrocardiogram (ECG) sensorRandom Number Generators (RNGs)waveletWireless Sensor Networks (WSNs)

Identifiers

PMID30134589
PMCPMC6164852

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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.