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Optimizing Energy-Cognitive Data Routing in Wireless Sensor Networks Using a Hybrid DRL-SI Approach with Levy Flight-Tuned Red Deer Optimization

Journal of Robotics and Control (JRC) · Vol. 7 No. 3 (2026) · hlm. 3909–3920

DOI
10.18196/jrc.v7i3.26189
ISSN
2715-5072, 2715-5056
Terbit
2026-09-29
Penerbit
Universitas Muhammadiyah Yogyakarta
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Energy efficiency and site reliability in data provision are two issues that are pinpointed as vital concerns to Wireless Sensor Networks (WSNs) because of limited resources and dynamic environment. A multimodel is hybrid Deep Reinforcement Learning (DRL) and Swarm Intelligence (SI) with a Levy Flight-tuned Red Deer Optimization (LFRDO) algorithm is proposed to meet these challenges. The DRL component is a tool used in real-time routing decisions depending on network states, e.g. residual energy and link quality. The SI module (Particle Swarm Optimization and Ant Colony Optimization) is applied in long term, global optimization of paths. The deployment of the LFRDO algorithm is made to provide exploration functions of the SI and to avoid an early convergence. An intensive simulation is conducted to check the model and comparisons are made with the A-Leach, Pegasus and DSO-EHO protocols. The results also show that the proposed approach is properly optimized and has an energy conservation value of 98%, 96 % packet delivery ratio and a network throughput of 0.9 Mbps in a 500-node network. End to end delay is also reduced. The paper notes that the proposed hybrid DRL-SI-LFRDO framework demonstrates significant improvement compared to the existing methods and can be an optimal robust and energy-cognitive routing solution to be used in the challenging WSNs that require, e.g., environmental monitoring and industrial automation.

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  • Universiti Teknikal Malaysia Melaka
  • Universiti Teknologi MARA
  • Symbiosis International University
  • Al-Farahidi University
  • University of Al-Ameed
  • Ministry of Education
  • Imam Ja’afar Al-Sadiq University
  • Organisasi lain
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Rekan penulisOrganisasiArtikel bersama
Norsuzila Ya’acobUniversiti Teknologi MARA3
Murizah Binti KassimUniversiti Teknologi MARA3
Reyad Omran EssaAl-Farahidi University3
Shafinaz IsmailUniversiti Teknikal Malaysia Melaka2
Azita Laily YusofUniversiti Teknologi MARA2
Siti Asma Che AzizUniversiti Teknikal Malaysia Melaka2
Aiman Zakwan JidinUniversiti Teknikal Malaysia Melaka2
Mohd Danial RozainiUniversiti Teknologi MARA2
Azlina IdrisUniversiti Teknologi MARA2
Ezmin AbdullahUniversiti Teknologi MARA2
Noraisyah TajudinUniversiti Teknologi MARA2
Nor Aizam AdnanUniversiti Teknologi MARA2
Pritesh ShahSymbiosis International University2
Amer IbrahimUnited Arab Emirates University2
Haleh AmintoosiFerdowsi University of Mashhad2
Juwita Mohd SultanUniversiti Teknikal Malaysia Melaka2
Yusmardiah YusufUniversiti Teknologi MARA1
Ahmad Fauzan KadminUniversiti Teknikal Malaysia Melaka1
Rostam Affendi HamzahUniversiti Teknikal Malaysia Melaka1
N. RahimUniversiti Teknikal Malaysia Melaka1
I. F. A. AzizUniversiti Teknikal Malaysia Melaka1
Mohd Saad HamidUniversiti Teknikal Malaysia Melaka1
Norain RahimUniversiti Teknikal Malaysia Melaka1
Irna Nadira MahzanUniversiti Teknikal Malaysia Melaka1
A Shamsul Rahimi A SubkiUniversiti Teknikal Malaysia Melaka1
Sattar AL-IbraheemiAfiliasi tidak diketahui1
Aliya Syahira Mohd AnuarUniversiti Teknologi MARA1
Wan Norsyafizan W. MuhamadUniversiti Teknologi MARA1
Shajahan MaidinUniversiti Teknikal Malaysia Melaka1
Mohd Kamarulnizam Abdul RahimAfiliasi tidak diketahui1
Norakmar Arbain SulaimanUniversiti Teknologi MARA1
Shuria SaaidinUniversiti Teknologi MARA1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.umy.ac.id/index.php/jrc/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Optimizing Energy-Cognitive Data Routing in Wireless Sensor Networks Using a Hybrid DRL-SI Approach with Levy Flight-Tuned Red Deer Optimization</dc:title>
	<dc:creator>Fuqdan A. Al-Ibraheemi</dc:creator>
	<dc:creator>Mushtaq Talb Tally</dc:creator>
	<dc:creator>Intisar A.M. Al Sayed</dc:creator>
	<dc:creator>Nadweh, Safwan</dc:creator>
	<dc:creator>Ravi Sekhar</dc:creator>
	<dc:creator>Karam S. Khalid</dc:creator>
	<dc:creator>Wan Haszerila Wan Hassan</dc:creator>
	<dc:creator>Darmawaty Mohd Ali</dc:creator>
	<dc:subject xml:lang="en-US">Wireless Sensor Networks</dc:subject>
	<dc:subject xml:lang="en-US">Energy-Aware Routing</dc:subject>
	<dc:subject xml:lang="en-US">Deep Reinforcement Learning</dc:subject>
	<dc:subject xml:lang="en-US">Swarm Intelligence</dc:subject>
	<dc:subject xml:lang="en-US">Red Deer Optimization</dc:subject>
	<dc:subject xml:lang="en-US">Levy Flight</dc:subject>
	<dc:description xml:lang="en-US">Energy efficiency and site reliability in data provision are two issues that are pinpointed as vital concerns to Wireless Sensor Networks (WSNs) because of limited resources and dynamic environment. A multimodel is hybrid Deep Reinforcement Learning (DRL) and Swarm Intelligence (SI) with a Levy Flight-tuned Red Deer Optimization (LFRDO) algorithm is proposed to meet these challenges. The DRL component is a tool used in real-time routing decisions depending on network states, e.g. residual energy and link quality. The SI module (Particle Swarm Optimization and Ant Colony Optimization) is applied in long term, global optimization of paths. The deployment of the LFRDO algorithm is made to provide exploration functions of the SI and to avoid an early convergence. An intensive simulation is conducted to check the model and comparisons are made with the A-Leach, Pegasus and DSO-EHO protocols. The results also show that the proposed approach is properly optimized and has an energy conservation value of 98%, 96 % packet delivery ratio and a network throughput of 0.9 Mbps in a 500-node network. End to end delay is also reduced. The paper notes that the proposed hybrid DRL-SI-LFRDO framework demonstrates significant improvement compared to the existing methods and can be an optimal robust and energy-cognitive routing solution to be used in the challenging WSNs that require, e.g., environmental monitoring and industrial automation.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Yogyakarta</dc:publisher>
	<dc:date>2026-09-29</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.umy.ac.id/index.php/jrc/article/view/26189</dc:identifier>
	<dc:identifier>10.18196/jrc.v7i3.26189</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Robotics and Control (JRC); Vol. 7 No. 3 (2026); 3909-3920</dc:source>
	<dc:source>2715-5072</dc:source>
	<dc:source>2715-5056</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.umy.ac.id/index.php/jrc/article/view/26189/12718</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Fuqdan A. Al-Ibraheemi, Mushtaq Talb Tally, Intisar A.M. Al Sayed, Safwan Nadweh, Ravi Sekhar, Karam S. Khalid, Wan Haszerila Wan Hassan, Darmawaty Mohd Ali</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>