A New Approach for Resource Recommendation in the Fog-Based IoT Using a Hybrid Algorithm.

This article focuses on improving resource recommendation in fog-based Internet of Things (IoT) environments by proposing a hybrid meta-heuristic algorithm combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), referred to as GAPSO. Fog-based IoT integrates computing, storage, and networking resources closer to end devices to reduce latency and bandwidth usage, but faces challenges in resource management due to its dynamic and heterogeneous nature. The GAPSO method aims to enhance recommendation accuracy and reduce response time compared to existing approaches such as Cooperative Filtering Smoothing and Fusing (CFSF) and Artificial Bee Colony (ABC) algorithms, as demonstrated through simulations using the CloudSim environment and the MovieLens dataset. Results indicate that GAPSO improves accuracy by approximately 1–8% and offers better response times, though it requires significant computational resources and longer simulation times. The article also outlines future research directions including integration with blockchain, deep learning, and improved auto-scaling mechanisms for fog-IoT systems.