Golden Sine Strategy-Fire Hawk Optimization for Energy-Efficient Resource Allocation in IoT-Based Cloud-Fog Environments

The rapid growth of Internet of Things (IoT) has created significant demand for effective resource management in cloud-fog platforms. Therefore, there is a need for efficient resource allocation in the IoT environment to avoid unnecessary delays. In this paper, a novel method named Golden Sine Strategy-Fire Hawk Optimization (GSS-FHO) is proposed for resource allocation in IoT-based cloud-fog systems. GSS-FHO minimizes latency, improves resource usage, and decreases energy consumption by optimally allocating resources to end users. It also overcomes challenges of existing metaheuristic optimization algorithms by integrating golden ratio and sine functions, while also enabling balanced exploration-exploitation, thus improving the overall performance and solving the local optimum issues. Additionally, the performance of GSS-FHO is compared with that of the conventional algorithms like Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) for analysis. The experimental results showcase the effectiveness of GSS-FHO as a reliable solution for dynamic cloud-fog platforms. When compared to existing methods, GSS-FHO reduces the load to 0.003, energy consumption to 0.12, and migration cost to 0.002, thereby demonstrating the proposed method's effective strategy for resource allocation.