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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) · pp. 3909–3920

DOI
10.18196/jrc.v7i3.26189
ISSN
2715-5072, 2715-5056
Published
2026-09-29
Publisher
Universitas Muhammadiyah Yogyakarta
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

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.

Keywords

Collaboration network of the authors

40 authors · 107 co-authorship links · 7 across different organisations · showing the 40 most connected

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

  • Universiti Teknikal Malaysia Melaka
  • Universiti Teknologi MARA
  • University of Al-Ameed
  • Ferdowsi University of Mashhad
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Norsuzila Ya’acobUniversiti Teknologi MARA3
Ahmed Dheyaa RadhiUniversity of Al-Ameed3
Amer IbrahimAffiliation unknown3
Shafinaz IsmailUniversiti Teknikal Malaysia Melaka2
Azita Laily YusofUniversiti Teknologi MARA2
Siti Asma Che AzizUniversiti Teknikal Malaysia Melaka2
Aiman Zakwan JidinUniversiti Teknikal Malaysia Melaka2
Noraisyah TajudinUniversiti Teknologi MARA2
Nor Aizam AdnanUniversiti Teknologi MARA2
Pritesh ShahAffiliation unknown2
Nouby M. GhazalyAffiliation unknown2
Husam Ali HadiAffiliation unknown2
Abdallah KassemAffiliation unknown2
Hassan AmoudAffiliation unknown2
Reyad Omran EssaAffiliation unknown2
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
Mohd Danial RozainiUniversiti Teknologi MARA1
Azlina IdrisUniversiti Teknologi MARA1
Ezmin AbdullahUniversiti Teknologi MARA1
Sattar AL-IbraheemiAffiliation unknown1
Aliya Syahira Mohd AnuarUniversiti Teknologi MARA1
Wan Norsyafizan W. MuhamadUniversiti Teknologi MARA1

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.

Metadata source: OAI-PMH (metadata jurnal) https://journal.umy.ac.id/index.php/jrc/oai, first harvested 9 Nov 2026, last seen 9 Nov 2026.
Original metadata (oai_dc) 
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	<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>