Fog device virtualization is a critical research area that focuses on abstracting and managing the computational, storage, and networking resources of fog nodes to enable efficient, flexible, and scalable deployment of applications and services. Research papers in this domain explore virtualization techniques such as virtual machines (VMs), containers, and unikernels to optimize resource utilization, reduce latency, and enhance Quality of Service (QoS) in heterogeneous fog computing environments. Studies highlight resource allocation, isolation, migration, and orchestration strategies that leverage heuristic algorithms, optimization models, and machine learning techniques—including reinforcement learning and predictive analytics—for intelligent and adaptive virtualized resource management. Recent works also investigate multi-tier fog–edge–cloud architectures to improve scalability, fault tolerance, and service continuity while minimizing virtualization overhead. Security- and privacy-aware virtualization frameworks are increasingly emphasized to ensure that virtualized fog resources maintain data confidentiality and system reliability. Applications span smart healthcare, autonomous vehicles, industrial IoT, smart cities, and latency-sensitive multimedia services, where virtualization enables flexible, efficient, and resilient fog infrastructures. Overall, research in fog device virtualization provides the foundation for adaptive, high-performance, and energy-efficient management of distributed fog resources, supporting next-generation edge and fog computing applications.