Multi-Objective Optimization for Link Stability in IoT-Fog-Cloud Architectures

Abstract

The integration of Internet of Things (IoT) devices with Fog and Cloud architectures has revolutionized data processing and communication, but maintaining link stability in such heterogeneous environments remains a critical challenge. This research proposes a multi-objective optimization framework aimed at enhancing link stability while balancing energy efficiency and latency in IoT-Fog-Cloud architectures. The model incorporates advanced optimization algorithms, including Pareto-based approaches, to address conflicting objectives such as minimizing energy consumption, maximizing throughput, and ensuring stable communication links. By leveraging real-time data from fog nodes and IoT devices, the framework dynamically adjusts resource allocation and routing decisions, ensuring efficient utilization of computational and network resources. Simulation results demonstrate that the proposed optimization model achieves significant improvements, including a 35% increase in link stability, a 20% reduction in energy consumption, and a 25% decrease in communication latency compared to conventional methods. The inclusion of a reliability metric ensures robust performance, with the system maintaining stability under varying network loads and mobility patterns. Furthermore, the framework supports scalability, making it suitable for large-scale deployments in domains such as smart cities, healthcare, and industrial automation. The results highlight the potential of multi-objective optimization to overcome the inherent trade-offs in IoT-Fog-Cloud systems, providing a reliable and efficient solution for real-time applications. Future research will focus on integrating adaptive learning mechanisms and expanding the model to incorporate emerging technologies like blockchain and 6G for enhanced security and performance