Improving Fog Gateway with Novel Metaheuristic-Driven AI Technique for Lessening the Delay and Energy Measures
Keywords:
Artificial Intelligence (AI),Energy Use, Fog Gateway, Internet of Things (Iot), Latency, Novel Snow Ablation Search Driven Catboost (SAS-CB)Abstract
The increasing need for quick data transmission and energy efficiency at the edge of the network has led to the development of a technology known as fog computing. Fog gateways are crucial elements in the architecture, as they offer computational capacity and enhance accessibility to end users and Internet of Things (IoT) devices for data-absorbing tasks. Improving latency and improving energy efficiency at the Fog Gateway remains a significant challenge. This research proposes a framework utilizing metaheuristic-driven artificial intelligence (AI) techniques to address the problem. This work introduces an innovative snow ablation search-driven catboost (SAS-CB) approach for identifying computational demands. The information from the IoT-driven fog computing system is utilized to develop the proposed SAS-CB method. We utilized sensors to gather environmental information for this research. Further feature selection is carried out utilizing the snow ablation optimization (SAO) technique to decrease the misinterpretation rate of the CB technique. The proposed method is implemented on a Python platform and evaluated based on several metrics such as utilization of energy (9W), latency (20s), and accuracy (90.45%). The experimental evidence indicates that the suggested solution outperformed existing methods in enhancing the fog gateway with favorable energy and latency parameters.
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