Fog Computing

Fog computing brings computation and data storage closer to the edge of networks, aiming to reduce latency, bandwidth consumption, and reliance on centralized cloud infrastructure. Current research emphasizes efficient resource management within these decentralized environments, focusing on algorithms like distributed genetic algorithms and multi-agent reinforcement learning for load balancing and task scheduling, as well as the application of deep learning models (e.g., CNNs, GNNs) for various tasks like image analysis and IoT data processing. This approach is particularly significant for applications requiring real-time processing of sensitive data, such as in healthcare and industrial automation, offering improvements in performance, privacy, and cost-effectiveness.