Research on Adaptive Fog Computing focuses on designing intelligent and flexible fog systems that can dynamically adjust resource allocation, task scheduling, and service provisioning in response to changing workloads, network conditions, and user requirements. This area addresses challenges arising from heterogeneous devices, mobility, fluctuating IoT traffic, and real-time processing needs. Key research directions include adaptive resource management and task scheduling algorithms, context-aware and workload-aware service provisioning, and predictive analytics using machine learning for proactive adaptation. Other emerging topics involve energy- and cost-aware adaptive strategies, fault-tolerant and self-healing fog infrastructures, edge–fog–cloud collaborative frameworks, and QoS- and SLA-aware adaptation mechanisms. Additionally, research on scalable, real-time, and intelligent adaptation techniques, container- or microservices-based adaptive deployment, and security- and privacy-aware adaptive fog systems represents significant avenues for advancing responsive, efficient, and resilient fog computing environments.