Evidence map›Paper›PMID 36904740›Full record

ArticleSensors (Basel, Switzerland)2023

TLS Protocol Analysis Using IoTST-An IoT Benchmark Based on Scheduler Traces.

Rafael Salles, Ricardo Farias

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Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Rafael SallesSystems Engineering and Computer Science Program (PESC/COPPE/UFRJ), Federal University of Rio de Janeiro, Rio de Janeiro 21941-972, Brazil.ORCID 0000-0002-4870-5259
Ricardo FariasSystems Engineering and Computer Science Program (PESC/COPPE/UFRJ), Federal University of Rio de Janeiro, Rio de Janeiro 21941-972, Brazil.ORCID 0000-0002-2411-3101

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Internet of Things (IoT) envisions billions of everyday objects sharing information. As new devices, applications and communication protocols are proposed for the IoT context, their evaluation, comparison, tuning and optimization become crucial and raise the need for a proper benchmark. While edge computing aims to provide network efficiency by distributed computing, this article moves towards sensor nodes in order to explore efficiency in the local processing performed by IoT devices. We present IoTST, a benchmark based on per-processor synchronized stack traces with the isolation and precise determination of the introduced overhead. It produces comparable detailed results and assists in determining the configuration that has the best processing operating point so that energy efficiency can also be considered. On benchmarking applications which involve network communication, the results can be influenced by the constant changes that occur in the state of the network. In order to circumvent such problems, different considerations or assumptions were used in the generalization experiments and the comparison to similar studies. To present IoTST usage on a real problem, we implemented it on a commercial off the-shelf (COTS) device and benchmarked a communication protocol, producing comparable results that are unaffected by the current network state. We evaluated different Transport-Layer Security (TLS) 1.3 handshake cipher suites at different frequencies and with various numbers of cores. Among other results, we could determine that the selection of a specific suite (Curve25519 and RSA) can improve the computation latency by up to four times over the worst suite candidate (P-256 and ECDSA), while both providing the same security level (128 bits).

Indexed as

ECCECDHEECDSAESP32IoT benchmarkingRSATLS 1.3

Identifiers

PMID36904740
PMCPMC10006961

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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.