Intelligent Energy Efficient Resource Allocation for Fog Computing in IoT

The Internet of Things (IoT) has increased data generation and processing requirements, leading to increased energy consumption and resource allocation in data centers.This research proposes an advanced energy-efficient resource optimization framework for fog computing data centers, integrating Q-Learning with threshold-based virtual machine migration for real-time resource management.This dual strategy enhances energy efficiency, reduces resource contention, and improves system scalability.The proposed method outperforms traditional methods, achieving 30.3% energy savings, 20% throughput improvement, and 15% reduction in response time.The sensitivity analysis on Q-learning hyperparameters provides theoretical justification for their selection.The findings highlight the scalability, adaptability, and real-world applicability of the proposed framework.