Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 1

Adaptive multi-radio quality of service model using neural network approach for robust wireless sensor network transmission in multipath fading environment

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 15 No. 4 (2025)

DOI
10.11591/ijece.v15i4.pp3795-3802
ISSN
2722-2578, 2088-8708
Published
2025-08-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Wireless sensor network loss in wireless data transmission is one of the problems that needs attention. Interference, fading, congestion, and delay are some factors that cause loss in wireless data transmission. This paper used an adaptive multi-radio model to enhance the wireless data transmission to be more robust to disturbance in a multipath fading environment. A neural network approach was used to generate the adaptive model. If we use 433 MHz as our carrier frequency with 250 kHz bandwidth and 12 spreading factors, we can get signal noise ratio (SNR) for 20 meters at about -9.8 dB. Thus, we can use the adaptive model to enhance the WSN wireless data transmission's SNR to 9 dB, automatically changing the radio configuration to 797.1 MHz frequency, with 378.1 bandwidth and 7.111 for spreading factor. Based on the result, the wireless data transmission link has been successfully enhanced using the proposed adaptive model for wireless sensor networks (WSN) in a multipath fading environment.

Collaboration network of the authors

36 authors · 114 co-authorship links · 48 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • University of Technology Malaysia
  • Mercu Buana University
  • Tun Hussein Onn University of Malaysia
  • Sultan Ageng Tirtayasa University
  • Université Rennes 1
  • Universitas 17 Agustus 1945 Jakarta
  • Pakuan University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
M. K. A. RahimUniversity of Technology Malaysia10
Farid ZubirUniversity of Technology Malaysia3
Noor Asmawati SamsuriUniversity of Technology Malaysia2
Mohd Fairus Mohd YusoffUniversity of Technology Malaysia2
Samsul Haimi DahlanTun Hussein Onn University of Malaysia2
Zuhairiah Zainal AbidinTun Hussein Onn University of Malaysia2
Bashar A. F. EsmailTun Hussein Onn University of Malaysia2
M. R. HamidUniversity of Technology Malaysia2
Raimi DewanUniversity of Technology Malaysia2
Norsaidah Muhamad NadzirUniversity of Technology Malaysia1
Amerrul ZabriUniversity of Technology Malaysia1
Nurul Jannah RamlyUniversity of Technology Malaysia1
Arrauzah RazakUniversity of Technology Malaysia1
Noor Asniza MuradUniversity of Technology Malaysia1
Ronan SauleauUniversité Rennes 11
Zaharah JohariUniversity of Technology Malaysia1
Mohamad Aasyraaf Mohd ShaufiTun Hussein Onn University of Malaysia1
Osman AyopTun Hussein Onn University of Malaysia1
M. Safuan ZainudinUniversity of Technology Malaysia1
Kamilia KamardinUniversity of Technology Malaysia1
Izni Husna IdrisUniversity of Technology Malaysia1
N. S. M. YazizUniversity of Technology Malaysia1
N. S. NadzirUniversity of Technology Malaysia1
Teguh FirmansyahSultan Ageng Tirtayasa University1
Supriyanto PraptodiyonoSultan Ageng Tirtayasa University1
Herudin HerudinSultan Ageng Tirtayasa University1
Didik AribowoSultan Ageng Tirtayasa University1
Syah AlamUniversitas 17 Agustus 1945 Jakarta1
Muchamad YunusPakuan University1
Mudrik AlaydrusMercu Buana University1
Rizki Ramadhan PutraMercu Buana University1
Muslim MuslimMercu Buana University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Adaptive multi-radio quality of service model using neural network approach for robust wireless sensor network transmission in multipath fading environment</dc:title><dc:creator>Hakim, Galang Persada Nurani</dc:creator><dc:creator>Astuti, Dian Widi</dc:creator><dc:creator>Firdausi, Ahmad</dc:creator><dc:creator>Majid, Huda A.</dc:creator><dc:description>&lt;jats:p&gt;Wireless sensor network loss in wireless data transmission is one of the problems that needs attention. Interference, fading, congestion, and delay are some factors that cause loss in wireless data transmission. This paper used an adaptive multi-radio model to enhance the wireless data transmission to be more robust to disturbance in a multipath fading environment. A neural network approach was used to generate the adaptive model. If we use 433 MHz as our carrier frequency with 250 kHz bandwidth and 12 spreading factors, we can get signal noise ratio (SNR) for 20 meters at about -9.8 dB. Thus, we can use the adaptive model to enhance the WSN wireless data transmission's SNR to 9 dB, automatically changing the radio configuration to 797.1 MHz frequency, with 378.1 bandwidth and 7.111 for spreading factor. Based on the result, the wireless data transmission link has been successfully enhanced using the proposed adaptive model for wireless sensor networks (WSN) in a multipath fading environment.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2025-08-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v15i4.pp3795-3802</dc:identifier><dc:identifier>10.11591/ijece.v15i4.pp3795-3802</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 15 No 4 (2025); 3795</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/37093/18406</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>