Fog computing extends cloud computing to the network edge, providing data, computation, storage, and application services to end-users. This article explores the motivation, advantages, and applications of fog computing in scenarios such as Smart Grid, smart traffic lights, and software-defined networks. It discusses the state-of-the-art of fog computing and similar work, and highlights security and privacy issues. Fog computing is particularly suitable for latency-sensitive applications, offering low latency, location awareness, and improved quality of service for streaming and real-time applications. Fog computing supports heterogeneity, with devices including end-user devices, access points, and edge routers. It is well-suited for real-time big data analytics and provides advantages in entertainment, advertising, and personal computing. Fog computing is implemented at the network edge, enabling applications to run closer to data sources. It supports mobility and dense geographical distribution. The article examines various scenarios where fog computing is applied, including Smart Grid, smart traffic lights, wireless sensor networks, and software-defined networks. In Smart Grid, fog devices process data from grid sensors and issue control commands to actuators. In smart traffic lights, fog devices coordinate to create green traffic waves and send warning signals to vehicles. In wireless sensor networks, fog devices control measurement processes and stability. In decentralized smart building control, fog devices enable distributed decision-making and activation. Fog computing is also applied in IoT and cyber-physical systems, where it supports embedded systems and integrates software and networking with physical environments. In software-defined networks (SDN), fog computing helps address issues in vehicular networks, such as intermittent connectivity and high packet loss rate, by augmenting vehicle-to-vehicle with vehicle-to-infrastructure communications. Security and privacy issues in fog computing are discussed, with a focus on man-in-the-middle attacks. These attacks can be stealthy, consuming minimal resources on fog devices. The article presents an example of a man-in-the-middle attack, where compromised gateways can hijack and replay communication between users. Traditional anomaly detection methods may not detect such attacks due to their low resource consumption. The article concludes that future work is needed to address security issues in fog computing, particularly in protecting fog devices from compromise and ensuring secure communication.Fog computing extends cloud computing to the network edge, providing data, computation, storage, and application services to end-users. This article explores the motivation, advantages, and applications of fog computing in scenarios such as Smart Grid, smart traffic lights, and software-defined networks. It discusses the state-of-the-art of fog computing and similar work, and highlights security and privacy issues. Fog computing is particularly suitable for latency-sensitive applications, offering low latency, location awareness, and improved quality of service for streaming and real-time applications. Fog computing supports heterogeneity, with devices including end-user devices, access points, and edge routers. It is well-suited for real-time big data analytics and provides advantages in entertainment, advertising, and personal computing. Fog computing is implemented at the network edge, enabling applications to run closer to data sources. It supports mobility and dense geographical distribution. The article examines various scenarios where fog computing is applied, including Smart Grid, smart traffic lights, wireless sensor networks, and software-defined networks. In Smart Grid, fog devices process data from grid sensors and issue control commands to actuators. In smart traffic lights, fog devices coordinate to create green traffic waves and send warning signals to vehicles. In wireless sensor networks, fog devices control measurement processes and stability. In decentralized smart building control, fog devices enable distributed decision-making and activation. Fog computing is also applied in IoT and cyber-physical systems, where it supports embedded systems and integrates software and networking with physical environments. In software-defined networks (SDN), fog computing helps address issues in vehicular networks, such as intermittent connectivity and high packet loss rate, by augmenting vehicle-to-vehicle with vehicle-to-infrastructure communications. Security and privacy issues in fog computing are discussed, with a focus on man-in-the-middle attacks. These attacks can be stealthy, consuming minimal resources on fog devices. The article presents an example of a man-in-the-middle attack, where compromised gateways can hijack and replay communication between users. Traditional anomaly detection methods may not detect such attacks due to their low resource consumption. The article concludes that future work is needed to address security issues in fog computing, particularly in protecting fog devices from compromise and ensuring secure communication.