Automatic spread factor and position definition for UAV gateway through computational intelligence approach to maximize signal-to-noise ratio in wooded environments

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PeerJ Computer Science

Main article text

 

Introduction

Methodology

Study environment

Data collect

Neural model training

Grey wolf optimizer

Wolf hierarchy

Search and attack prey

Surround the prey

Hunt the prey

Grey wolf optimizer modeling

Restrictions

Optimization problem

Results

Neural network

Optimizer

Conclusion

Supplemental Information

Dataset and codes used in the research.

The code (otimizador_ufpa) to run the adapted gwo optimizer for the UFPA scenario.

- code "main.m" runs the optimizer

- code "trata_resultados.m" plots the general analysis for all paretos

- folder "resultados" contains the plots used in the draft and the results

- obtained for the optimizer in .mat files

The code (SNR_mlp) to train the grnn and mlp networks:

- "old_mlp_snr_v2.m" trains the mlp networks

- "grnn_snr.m" trains the grnn networks

- "analise_resultados.m" generates the boxplot to compare grnn x mlp, then find the best network

DOI: 10.7717/peerj-cs.2237/supp-1

Measured RSSI and SNR.

DOI: 10.7717/peerj-cs.2237/supp-2

GWO convergence for Pareto 2.

DOI: 10.7717/peerj-cs.2237/supp-3

GWO convergence for Pareto 3.

DOI: 10.7717/peerj-cs.2237/supp-4

GWO convergence for Pareto 4.

DOI: 10.7717/peerj-cs.2237/supp-5

GWO convergence for Pareto 5.

DOI: 10.7717/peerj-cs.2237/supp-6

GWO convergence for Pareto 6.

DOI: 10.7717/peerj-cs.2237/supp-7

GWO convergence for Pareto 7.

DOI: 10.7717/peerj-cs.2237/supp-8

GWO convergence for Pareto 8.

DOI: 10.7717/peerj-cs.2237/supp-9

GWO convergence for Pareto 9.

DOI: 10.7717/peerj-cs.2237/supp-10

GWO final positioning for Pareto 2.

DOI: 10.7717/peerj-cs.2237/supp-11

GWO final positioning for Pareto 3.

DOI: 10.7717/peerj-cs.2237/supp-12

GWO final positioning for Pareto 4.

DOI: 10.7717/peerj-cs.2237/supp-13

GWO final positioning for Pareto 5.

DOI: 10.7717/peerj-cs.2237/supp-14

GWO final positioning for Pareto 6.

DOI: 10.7717/peerj-cs.2237/supp-15

GWO final positioning for Pareto 9.

DOI: 10.7717/peerj-cs.2237/supp-16

Additional Information and Declarations

Competing Interests

The authors declare that they have no competing interests.

Author Contributions

Caio M. M. Cardoso conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Alex S. Macedo performed the experiments, analyzed the data, prepared figures and/or tables, and approved the final draft.

Filipe Cavalcanti Fernandes performed the computation work, prepared figures and/or tables, and approved the final draft.

Hugo A. O. Cruz performed the computation work, authored or reviewed drafts of the article, and approved the final draft.

Fabrício J. B. Barros conceived and designed the experiments, authored or reviewed drafts of the article, and approved the final draft.

Jasmine P. L. de Araújo conceived and designed the experiments, authored or reviewed drafts of the article, and approved the final draft.

Data Availability

The following information was supplied regarding data availability:

The data and code are available in the Supplemental File.

Funding

This study was financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)–Finance Code 001. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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