Research on Real-time Data Analytics in Edge Gateways focuses on designing frameworks and algorithms to process, filter, and analyze data streams locally at edge gateways, enabling low-latency decision-making and reducing dependency on centralized cloud servers. This area addresses challenges such as heterogeneous IoT devices, high-volume and high-velocity data streams, limited computational and storage resources, and the need for real-time responsiveness. Key research directions include streaming data processing frameworks, edge-based aggregation and preprocessing techniques, and machine learning–driven real-time analytics for anomaly detection, predictive maintenance, and context-aware services. Other emerging topics involve adaptive and scalable edge gateway architectures, energy- and resource-aware analytics, and edge–cloud collaborative processing for enhanced accuracy and efficiency. Additionally, research on privacy-preserving and secure real-time analytics, fault-tolerant data pipelines, and multi-objective optimization for latency, throughput, and resource utilization represents significant avenues for advancing intelligent, efficient, and resilient edge gateway systems.