Evidence map›Paper›PMID 35602637›Full record

ArticleComputational intelligence and neuroscience2022

Swarm Intelligence with Adaptive Neuro-Fuzzy Inference System-Based Routing Protocol for Clustered Wireless Sensor Networks.

Feras Mohammed A-Matarneh, Bassam A Y Alqaralleh, Fahad Aldhaban, Esam A AlQaralleh, Anil Kumar, Deepak Gupta, Gyanendra Prasad Joshi

RetractedOpen access · hybridAbstract readRetracted Publication
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.6field-weighted citation impact, top 17% of its field
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

1 citing paper in PubMed, 11 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 6 institutions in 4 countries.

Feras Mohammed A-MatarnehDepartment of Computer Science, University College of Duba, University of Tabuk, Tabuk 71491, Saudi Arabia.ORCID https://orcid.org/0000-0002-7847-7314
Bassam A Y AlqarallehMIS Department, College of Business Administration, University of Business and Technology, Jeddah 21448, Saudi Arabia.ORCID https://orcid.org/0000-0003-2232-9387
Fahad AldhabanMIS Department, College of Business Administration, University of Business and Technology, Jeddah 21448, Saudi Arabia.ORCID https://orcid.org/0000-0001-9963-6049
Esam A AlQarallehSchool of Engineering, Princess Sumaya University for Technology, Amman 11941, Jordan.ORCID https://orcid.org/0000-0001-7245-7964
Anil KumarData Science Research Group, School of Computing, DIT University, Dehradun, India.ORCID https://orcid.org/0000-0003-0982-9424
Deepak GuptaDepartment of Computer Science & Engineering, Maharaja Agrasen Institute of Technology, Delhi, India.ORCID https://orcid.org/0000-0002-3019-7161
Gyanendra Prasad JoshiDepartment of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea.ORCID https://orcid.org/0000-0002-5446-288X
University of Business and Technology · SADehradun Institute of Technology University · INMaharaja Engineering College · INPrincess Sumaya University for Technology · JOSejong University · KRUniversity of Tabuk · SA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wireless sensor network (WSN) comprises numerous compact-sized sensor nodes which are linked to one another. Lifetime maximization of WSN is considered a challenging problem in the design of WSN since its energy-limited capacity of the inbuilt batteries exists in the sensor nodes. Earlier works have focused on the design of clustering and routing techniques to accomplish energy efficiency and thereby result in an increased lifetime of the network. The multihop route selection process can be treated as an NP-hard problem and can be solved by the use of computational intelligence techniques such as fuzzy logic and swarm intelligence (SI) algorithms. With this motivation, this article aims to focus on the design of swarm intelligence with an adaptive neuro-fuzzy inference system-based routing (SI-ANFISR) protocol for clustered WSN. The proposed SI-ANFISR technique aims to determine the cluster heads (CHs) and optimal routes for multihop communication in the network. To accomplish this, the SI-ANFISR technique primarily employs a weighted clustering algorithm to elect CHs and construct clusters. Besides, the SI-ANFISR technique involves the design of an ANFIS model for the selection process, which make use of three input parameters, namely, residual energy, node degree, and node history. In order to optimally adjust the membership function (MF) of the ANFIS model, the squirrel search algorithm (SSA) is utilized. None of the earlier works have used ANFIS with SSA for the routing process. The design of SSA to tune the MFs of the ANFIS model for optimal routing process in WSN shows the novelty of the study. The experimental validation of the SI-ANFISR technique takes place, and the results are inspected under different aspects. The simulation results highlighted the significant performance of the SI-ANFISR technique compared to the recent techniques with a maximum throughput of 43838 kbps and residual energy of 0.4800J, respectively.

Indexed as

Computer Communication NetworksWireless TechnologyCluster AnalysisFuzzy LogicIntelligence

Identifiers

PMID35602637
PMCPMC9122693
OpenAlexW4280514109

What OpenQuestion holds

Textmetadata
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.