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All Journal Asian Journal of Science, Technology, Engineering, and Art Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Abdulkareem, H. A.
Unknown Affiliation
Arts Computer Science & IT Engineering Mechanical Engineering Social Sciences Transportation

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Adaptive Q-Learning-Based Radio Resource Management Optimization in 5G and Beyond Heterogeneous-heterogeneous Networks: A Comprehensive Review Abdulkadir, Abubakar; Kabir, Mahmoud T.; Abdulkareem, H. A.; Abdullahi, ZM
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 1 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i1.4578

Abstract

This paper reviews advanced radio resource management (RRM) optimization techniques in 5G and beyond heterogeneous-heterogeneous networks (Het-HetNets). Key innovations include fairness-aware models for mmWave 5G, machine learning (ML)-driven traffic management, and game-theoretic approaches for interference mitigation in Massive MIMO systems. Blockchain technology emerges as a promising tool for secure spectrum sharing, while deep learning enhances handover management and resource allocation. Hybrid frameworks, such as deep reinforcement learning and non-orthogonal multiple access, address energy efficiency and quality of service (QoS) challenges for IoT, autonomous vehicles, and smart cities. Despite these advancements, challenges like scalability, computational complexity, and data privacy persist. Q-learning-based adaptive RRM frameworks demonstrate potential for optimizing energy and spectral efficiency by addressing dynamic network conditions. The integration of ML with blockchain enables secure and decentralized RRM. Critical research gaps identified include scalability, real-time deployment, and interference management in ultra-dense networks. This review highlights the importance of scalable, efficient, and adaptive solutions to advance the telecommunications system.
A review on Energy Consumption Model on Hierarchical clustering techniques for IoT- based multilevel heterogeneous WSNs using Energy Aware Node Selection. Iyobhebhe, Matthew; Tekanyi, Abdooulie Momodou. S.; Abubilal, K. A; Usman, Aliyu. D; Isiaku, Yau; Agbon, E. E; obi, Elvis; Chollom, Botson Ishaya; Ezugwu, Chukwudi; Eleshin, Ridwan. O.; Abdulkareem, H. A.; Ashafa, Fatima; Abubakar, Saba; Umar, Abubakar; Ajayi Ore-Ofe; Thomas Muge, Paul
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i2.34882

Abstract

This review article scrutinizes the energy consumption model related to hierarchical clustering methods in IoT-based multi-tier heterogeneous networks (WSNs). Since energy efficiency is vital to prolong the operational activities of sensor nodes, this review article concentrated on energy-aware node selection as a significant technique for improving energy consumption. The review article deliberates on the challenges posed by dynamic wireless sensor network conditions, node heterogeneity like energy-based, and scalability challenges that affect energy management. This review article scrutinizes the energy consumption model related to hierarchical clustering methods in IoT-based multi-tier heterogeneous networks (WSNs). Since energy efficiency is vital to prolong the operational activities of sensor nodes, this review article concentrated on energy-aware node selection as a significant technique for improving energy consumption. We scrutinize different factors affecting efficient node selection, comprising residual energy, transmission distance, and sensor node reliability while juxtaposing these techniques with traditional node selection schemes. Furthermore, the importance of developed modeling techniques was highlighted. Finally, future research directions were outlined, by accentuating the incorporation of energy harvesting and collective models to improve the stability and operation of Wireless Sensor Networks. This holistic overview aims to offer appreciated insights for authors and practitioners in WSNs.
Co-Authors Abdulkadir, Abubakar Abdullahi, ZM Abubakar, Saba Abubilal, K. A Agbon, E. E Ajayi Ore-Ofe Ashafa, Fatima Chollom, Botson Ishaya Eleshin, Ridwan. O. Ezugwu, Chukwudi Isiaku, Yau Iyobhebhe, Matthew Kabir, Mahmoud T. obi, Elvis Tekanyi, Abdooulie Momodou. S. Thomas Muge, Paul Umar, Abubakar Usman, Aliyu. D