Research on Optimization of Task Scheduling in Fog Computing focuses on designing strategies and algorithms to efficiently assign and manage tasks across distributed fog nodes, aiming to minimize latency, energy consumption, and execution cost while ensuring Quality of Service (QoS) and Service Level Agreement (SLA) compliance. This area addresses challenges posed by heterogeneous and resource-constrained devices, dynamic workloads, mobility of IoT devices, and real-time processing requirements. Key research directions include heuristic- and metaheuristic-based scheduling algorithms (e.g., genetic algorithms, particle swarm optimization, ant colony optimization), workload- and context-aware task scheduling, and predictive models for proactive task allocation. Other emerging topics involve energy-efficient and cost-aware scheduling, multi-objective optimization balancing latency, resource utilization, and reliability, cloud–fog–edge collaborative task scheduling, and container- or microservices-based scheduling frameworks. Additionally, research on adaptive, fault-tolerant, and machine learning-enhanced task scheduling techniques represents significant avenues for advancing intelligent, scalable, and efficient fog computing systems.