Review on Automated Meeting Request and Queue Management System for Efficient Administrative Coordination

Abstract

Efficient queue management is a critical requirement across diverse sectors, including healthcare, telecommunications, retail, and intelligent environments. This study presents a comprehensive Queue Management System (QMS) that integrates Artificial Intelligence (AI), the Internet of Things (IoT), and cloud-based analytics to enhance operational efficiency and user experience. By leveraging insights from various fields such as hospital queue management, network congestion control, and sensor-driven smart environments, the proposed system dynamically optimizes queue flow, reduces wait times, and improves resource utilization. AI-powered Active Queue Management (AQM) techniques facilitate efficient network traffic regulation, while IoT-based real-time monitoring enables proactive customer service in physical environments. Furthermore, mobile integration and cloud computing provide seamless queue tracking and predictive analytics for demand forecasting. This holistic approach ensures a scalable, adaptive, and industry-agnostic solution for effective queue management across multiple domains.