Energy-efficient resource provisioning in fog computing is a critical research area that focuses on allocating computational, storage, and networking resources across distributed fog nodes while minimizing energy consumption and maintaining Quality of Service (QoS). Research papers in this domain explore static, dynamic, and adaptive provisioning strategies that account for heterogeneous node capabilities, varying workloads, network conditions, and energy constraints. Studies highlight the use of heuristic algorithms, optimization models, metaheuristic approaches, and machine learning techniques—including reinforcement learning and predictive analytics—for intelligent, context-aware, and energy-efficient resource allocation. Recent works also investigate multi-tier fog–edge–cloud architectures to enhance scalability, fault tolerance, and service continuity while reducing operational energy costs. Security- and privacy-aware frameworks are increasingly integrated to ensure that energy-efficient provisioning does not compromise sensitive data or system reliability. Applications span smart healthcare, autonomous vehicles, industrial IoT, smart cities, and latency-sensitive multimedia services. Overall, research in energy-efficient resource provisioning in fog computing enables adaptive, sustainable, and high-performance management of distributed resources, ensuring optimized energy usage, reliability, and QoS in next-generation fog computing environments.