Teaching Learning-Based Optimization for Medical IoT applications Service Placement in Fog Computing

Fog Computing with Medical Internet of Things (IoT) applications changes how healthcare systems manage low latency, privacy, and reliability. This research suggests using Teaching Learning-Based Optimization (TLBO) to improve fog computing service placement for Medical IoT applications. Inspired by classroom teaching and learning, TLBO guides solutions to optimal configurations through collaboration. Service placement is crucial to fog node computational resource allocation for Medical IoT healthcare services. Optimizing medical service distribution across fog nodes reduces latency, improves privacy, and boosts healthcare system reliability. Simulations indicate the TLBO algorithm’s adaptability to dynamic and resource-constrained healthcare situations. The results show that TLBO-based service placement optimizes crucial healthcare parameters, opening up new Fog Computing applications in Medical IoT.