Profit-aware resource allocation in fog computing is a significant research area that focuses on optimizing the allocation of computational, storage, and network resources across distributed fog nodes while maximizing economic benefits for service providers and minimizing costs for users. Research papers in this domain explore strategies that consider heterogeneous fog infrastructure, dynamic workloads, Quality of Service (QoS) requirements, energy consumption, and operational costs to achieve profit-efficient resource management. Studies highlight the use of optimization algorithms, game-theoretic approaches, and machine learning techniques—including reinforcement learning and predictive analytics—for intelligent, adaptive, and context-aware resource allocation decisions. Recent works also investigate multi-tier fog–edge–cloud architectures to improve scalability, service continuity, and workload balancing while ensuring cost-effectiveness. Security- and privacy-aware frameworks are increasingly integrated to protect sensitive data without compromising profit-oriented resource utilization. Applications span autonomous vehicles, smart healthcare, industrial IoT, smart cities, and latency-sensitive multimedia services, where economic efficiency and service performance are critical. Overall, research in profit-aware resource allocation in fog computing enables adaptive, efficient, and cost-effective management of distributed resources, balancing performance, reliability, and economic objectives in next-generation fog computing environments.