Evidence map›Paper›PMID 36236264›Full record

ArticleSensors (Basel, Switzerland)2022

Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation.

Manikandan Thirumalaisamy, Shajahan Basheer, Shitharth Selvarajan, Sara A Althubiti, Fayadh Alenezi, Gautam Srivastava, Jerry Chun-Wei Lin

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. 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

7 authors.

Manikandan ThirumalaisamySchool of Computing Science and Engineering, Galgotias University, Greater Noida 203201, India.ORCID 0000-0002-8416-279X
Shajahan BasheerSchool of Computing Science and Engineering, Galgotias University, Greater Noida 203201, India.ORCID 0000-0001-7199-6595
Shitharth SelvarajanDepartment of Computer Science, Kebri Dehar University, Kebri Dehar P.O. Box 250, Ethiopia.ORCID 0000-0002-4931-724X
Sara A AlthubitiDepartment of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah 11952, Saudi Arabia.ORCID 0000-0002-9087-3010
Fayadh AleneziDepartment of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia.ORCID 0000-0002-4099-1254
Gautam SrivastavaDepartment of Mathematics and Computer Science, Brandon University, Brandon, MB R7A 6A9, Canada.ORCID 0000-0001-9851-4103
Jerry Chun-Wei LinDepartment of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway, University of Applied Sciences, 5063 Bergen, Norway.ORCID 0000-0001-8768-9709

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There can be many inherent issues in the process of managing cloud infrastructure and the platform of the cloud. The platform of the cloud manages cloud software and legality issues in making contracts. The platform also handles the process of managing cloud software services and legal contract-based segmentation. In this paper, we tackle these issues directly with some feasible solutions. For these constraints, the Averaged One-Dependence Estimators (AODE) classifier and the SELECT Applicable Only to Parallel Server (SELECT-APSL ASA) method are proposed to separate the data related to the place. ASA is made up of the AODE and SELECT Applicable Only to Parallel Server. The AODE classifier is used to separate the data from smart city data based on the hybrid data obfuscation technique. The data from the hybrid data obfuscation technique manages 50% of the raw data, and 50% of hospital data is masked using the proposed transmission. The analysis of energy consumption before the cryptosystem shows the total packet delivered by about 71.66% compared with existing algorithms. The analysis of energy consumption after cryptosystem assumption shows 47.34% consumption, compared to existing state-of-the-art algorithms. The average energy consumption before data obfuscation decreased by 2.47%, and the average energy consumption after data obfuscation was reduced by 9.90%. The analysis of the makespan time before data obfuscation decreased by 33.71%. Compared to existing state-of-the-art algorithms, the study of makespan time after data obfuscation decreased by 1.3%. These impressive results show the strength of our methodology.

Indexed as

AlgorithmsCloud ComputingSoftwareAODE classifiercloud computing separationdata classificationdata obfuscationdata storingdata transmission

Identifiers

PMID36236264
PMCPMC9572171

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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.