Exploring Hybrid AI Strategies for Intelligent Resource Management in Fog Computing: A Review

Abstract:-Fog computing has emerged as a pivotal paradigm in bridging the gap between cloud and edge computing, enabling efficient resource management and low-latency processing for IoT-driven applications. However, the dynamic nature of fog environments presents significant challenges in resource allocation, workload balancing, and energy efficiency. In response, hybrid Artificial Intelligence (AI)-based frameworks have gained prominence, integrating machine learning, deep learning, and heuristic optimization techniques to enhance decision-making and adaptability in fog computing ecosystems. This review provides an in-depth analysis of existing AI-driven strategies for optimized resource management in fog computing. It explores the synergy between rule-based heuristics, reinforcement learning, federated learning, and swarm intelligence in addressing computational and network constraints. Additionally, the paper highlights key research gaps, security considerations, and the potential of emerging AI-driven methodologies, such as neuromorphic computing and explainable AI, in transforming fog infrastructure

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