IoT Driven Queueing System: Analyzing Customer Behavior for Optimal Service Management

The integration of Internet of Things (IoT) technology in queueing systems has transformed customer service management by enabling real-time data collection, analysis, and decision-making. IoT-driven solutions facilitate efficient queue management, reduce wait times, and enhance customer satisfaction through predictive analytics and automated service adjustments. This paper synthesizes existing research on IoT-based queueing systems, focusing on customer behavior analysis and service management strategies. By leveraging IoT data analytics, businesses can implement optimal service management strategies such as service time optimization, intelligent staff allocation, and dynamic queue adjustments. Additionally, IoT-based queueing models help businesses predict congestion and improve service efficiency. This study also explores the role of artificial intelligence (AI) and machine learning (ML) in enhancing IoT-enabled queueing systems. Key challenges, including security, privacy concerns, and system scalability, are identified as critical areas for future research. The paper provides a comprehensive overview of advancements in IoT-driven queueing systems and their impact on customer behavior and service management, offering insights for researchers and industry practitioners.