Resource monitoring and allocation in fog computing is a vital research area that focuses on real-time tracking, management, and distribution of computational, storage, and network resources across distributed fog nodes to ensure optimal system performance, energy efficiency, and Quality of Service (QoS). Research papers in this domain explore mechanisms for continuous monitoring of resource utilization, workload dynamics, and network conditions, combined with intelligent allocation strategies to adapt to fluctuating demands and heterogeneous fog infrastructures. Studies highlight the use of heuristic algorithms, optimization models, multi-agent systems, and machine learning techniques—including reinforcement learning and predictive analytics—for adaptive and context-aware resource allocation. Recent works also investigate multi-tier fog–edge–cloud architectures to enhance scalability, fault tolerance, and service continuity while minimizing latency and resource contention. Security- and privacy-aware monitoring and allocation frameworks are increasingly emphasized to protect sensitive data and ensure reliable operations. Applications include smart healthcare, autonomous vehicles, industrial IoT, smart cities, and latency-sensitive multimedia services. Overall, research in resource monitoring and allocation in fog computing enables intelligent, adaptive, and efficient management of distributed resources, ensuring high-performance, resilient, and reliable fog computing systems.