Context-aware fog computing is a prominent research area that focuses on designing fog systems that can adapt intelligently to dynamic contexts such as user mobility, device heterogeneity, environmental conditions, workload variations, and application requirements. Research papers in this domain propose context-aware resource allocation, task scheduling, and service placement frameworks that leverage real-time context information to optimize latency, energy consumption, and Quality of Service (QoS). Studies explore the integration of context-awareness with machine learning, reinforcement learning, and predictive analytics to enable proactive decision-making in fog infrastructures. Recent works highlight multi-objective optimization that balances context-driven trade-offs, such as mobility support versus energy efficiency or cost versus QoE. Context-aware security and privacy mechanisms are also being developed to ensure reliable and trustworthy service delivery under varying environments. Applications of context-aware fog computing span smart healthcare (e.g., patient monitoring), intelligent transportation (e.g., vehicular fog computing), smart cities, industrial IoT, and AR/VR, where adapting to real-time context is critical for system efficiency and user satisfaction. Overall, research in context-aware fog computing enables fog infrastructures to deliver personalized, adaptive, and resilient services in dynamic and heterogeneous environments.