Research on IoT Data Processing in Fog Computing focuses on enabling efficient, low-latency, and scalable processing of the massive and heterogeneous data generated by IoT devices at the network edge. This area addresses challenges related to limited computational resources, dynamic workloads, network constraints, and real-time processing requirements. Key research directions include edge–fog–cloud collaborative data processing frameworks, distributed and parallel data analytics for IoT streams, and context-aware task scheduling for timely data handling. Other emerging topics involve energy-efficient and workload-aware IoT data processing, real-time anomaly detection and event processing, secure and privacy-preserving data analytics, and integration with machine learning for predictive and adaptive decision-making. Additionally, research on fog-based data aggregation, storage optimization, and microservices- or container-based architectures for IoT applications represents significant avenues for advancing intelligent, responsive, and scalable IoT data processing systems.