Integrating Multi‐Objective Metaheuristic Optimization and Graph‐Based Architectures for Enhanced Fog Computing in IoT Systems

The exponential growth of the Internet of Things (IoT) and Internet of Medical Things (IoMT) has imposed significant demands on cloud‐centric architectures, particularly in terms of latency, energy efficiency, and scalability. Fog computing emerges as a promising paradigm by decentralizing computation and storage closer to data sources. This paper presents and validates a unified framework that integrates multi‐objective metaheuristic optimization for computation offloading and task scheduling with graph‐based database architectures for real‐time IoMT data management. The key novelty lies in the system‐level integration of these components, creating a feedback‐aware architecture where scheduling decisions explicitly account for database performance, rather than proposing new optimization algorithms in isolation. The study leverages enhanced metaheuristic algorithms such as Multi‐objective Arithmetic Optimization Algorithm (MoAOA) and Henon‐Evoked Rhinopithecus Swarm Optimization (HERSOA) to optimize energy consumption, latency, and throughput while ensuring reliable task prioritization and resource allocation. A formal data‐layer latency model is introduced and validated, with the optimization framework explicitly incorporating database performance metrics. Through comprehensive simulation, ablation studies, and statistical validation, we demonstrate that the combination of advanced optimization techniques and graph‐based fog architectures significantly improves QoS parameters, system stability, and scalability in dynamic IoT environments. This work provides a foundational guideline for future research and practical deployments in smart healthcare, smart cities, and industrial IoT.