Review of AI-Driven and Blockchain-Enabled Solutions for Power Optimization in Next-Generation Wireless Networks

Authors

  • Shriganesh Yadav, Pravin Advivarekar, Shivshankar Kore, Gaurang Tawde, Ninisha Patil

Keywords:

5G, 6G,Energy Efficiency, Quality of Service (QoS), AI-Driven Optimization, Blockchain, Network Slicing, Reinforcement Learning, Edge Computing, Smart Contracts, Quantum Communications, Self-Healing Networks.

Abstract

Considering the rapid development of 5G networks, energy efficiency and QoS become major challenges.   Statistical QoS-driven power allocation and Markov Chain-based traffic distribution have improved network use, but they have not dynamically adjusted to meet real-time traffic needs.   Recent advances in blockchain-based safe energy transactions and AI-driven adaptive power management enable distributed energy trade, interference control, and real-time resource optimization.  This article compares 5G energy-efficient QoS methods, emphasizing the shift from static power allocation models to AI-powered real-time decision-making systems.   An AI and blockchain-integrated power optimization framework uses Reinforcement Learning (RL) for predictive power allocation, smart contracts for safe energy transactions, and edge computing for low-latency resource distribution.   The proposed architecture reduces power consumption by 30–50% while maintaining QoS and scalability for next 6G networks.  This paper addresses high computational complexity, security risks, and scalability constraints to create intelligent and sustainable 5G/6G networks.   Quantum-assisted communication, self-healing AI, and energy-efficient IoT networking are future research topics.

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Published

02.11.2024

How to Cite

Shriganesh Yadav. (2024). Review of AI-Driven and Blockchain-Enabled Solutions for Power Optimization in Next-Generation Wireless Networks. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 5545 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7456

Issue

Vol. 12 No. 4 (2024)

Section

Research Article

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