Research on Optimization of Resource Allocation in Fog Computing focuses on developing strategies and algorithms to efficiently distribute computational, storage, and network resources across heterogeneous and distributed fog nodes while ensuring low latency, high reliability, and Quality of Service (QoS). This area addresses challenges such as dynamic workloads, mobility of IoT devices, constrained resources, and real-time processing requirements. Key research directions include heuristic- and metaheuristic-based resource allocation algorithms (e.g., genetic algorithms, particle swarm optimization), workload- and context-aware resource management, and predictive analytics for proactive allocation. Other emerging topics involve energy- and cost-aware optimization, SLA-compliant resource provisioning, edge–fog–cloud collaborative frameworks, and container- or microservices-based orchestration for efficient utilization. Additionally, research on adaptive, fault-tolerant, and machine learning-enhanced resource allocation techniques represents significant avenues for advancing intelligent, scalable, and efficient fog computing systems.