IoT Integration and XGBoost Modeling for Faster Gas Station Service

Businesses must optimize service delivery to compete in today’s fast-paced environment. Gas stations and fuel hubs are under pressure to improve efficiency and customer service. This study suggests integrating the Internet of Things (IoT) with XGBoost modeling to simplify gas station operations and speed up service. First, IoT sensors are strategically placed across the gas station infrastructure to monitor fuel levels, traffic movement, and customer lines in real time. This thorough data gathering creates a dynamic operating environment for proactive decision-making. Second, XGBoost, a strong machine learning algorithm, analyzes IoT data to forecast consumer demand and traffic trends. XGBoost predicts accurately using past data and various factors, enabling resource allocation and service planning. Gas station operators profit from IoT and XGBoost modeling. Predictive maintenance speeds up fix and reduces downtime. It optimizes staffing plans to ensure enough workers during peak hours to speed up service. It also allows consumer behavior analysis-based, tailored marketing. The suggested method allows gas stations to adjust to shifting demand patterns, improve operational efficiency, and provide quicker, better service. Gas stations may lead in service and innovation by integrating IoT and sophisticated analytics.