Evidence map›Paper›PMID 36857320›Full record

ArticlePloS one2023

Novel intelligent architecture and approximate solution for future networks.

Akram Sarhan, Mahdi Jemmali

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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.

Akram SarhanDepartment of Information Technology, College of Computing and Information Technology at Khulis, University of Jeddah, Jeddah, Saudi Arabia.ORCID 0000-0002-1522-562X
Mahdi JemmaliDepartment of Computer Science and Information, College of Science at Zulfi, Majmaah University, AL-Majmaah, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Private networks have become popular for secure data sharing and anonymous communication in many domains: enterprise environments, military, journalism, telecommunication, healthcare, to name a few. It has been used with or without internet connection. Its primary purpose is to provide confidentiality, bypass unlawful activities, and protect against common threats such as interception, modification, and censorship. In addition, several private network technologies exist to support secure communications. However, they mostly rely on encryption only. The transmitted data is classified into different confidentiality levels. This research presents a smart private network architecture scheme that transmits constraint-based classified packets. The main directive of this work is the proposed constraint. This constraint is meant to enforce that if two packets belong to the same confidentiality level, they can't be transmitted through the two routers simultaneously. Therefore, the studied problem is an NP-hard problem. This paper presents the following contributions: (i) proposes a new architecture paradigm for outsourcing a constraint-based multi-classified data sharing securely and transmitted through two routers; (ii) introduces several algorithms to prove the feasibility for this NP-Hard problem; and (iii) implements the algorithms solutions using C++ and compares their performance. Different metrics are used to measure the performance of the proposed algorithms. Randomized Longest Transmission time first algorithm [Formula: see text] scored the best algorithm with a percentage of 73.5% and an average gap of 0.002 according to the experimental results. It is remarkable worthy to note that the execution time of all the algorithms is less than 0.001 s.

Indexed as

AlgorithmsBenchmarkingCommunicationHealth FacilitiesInformation Dissemination

Identifiers

PMID36857320
PMCPMC9977064

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.