Sensor-Enabled Context-Aware and Pro-Active Queue Management Systems in Intelligent Environments.

Area of Science:

• Computer Science
• Artificial Intelligence
• Internet of Things

Background:

• Queue systems present significant challenges in smart city environments, impacting customer satisfaction and operational efficiency.
• Effective queue management is crucial for economic benefits and improved service quality in various institutions.

Purpose of the Study:

• To propose an intelligent, proactive, and context-aware queue management system for smart cities.
• To enhance user-friendliness and client satisfaction through intelligent service delivery.

Main Methods:

• Development of a context-driven ecosystem utilizing multiple low-level sensors and Internet of Things (IoT) devices.
• Implementation of client behavior recognition and understanding for proactive support.
• Validation through formal analysis and simulation of a prototype system.

Main Results:

• The proposed system demonstrates user-friendliness and effective client behavior recognition.
• The prototype validation confirms the system's capability to process massive data streams.
• The system supports proactive actions to improve client experience.

Conclusions:

• The developed intelligent queue management system offers a viable solution for smart city challenges.
• The prototype serves as a valuable reference for future context-aware, proactive IoT systems.
• This approach enhances service quality and economic benefits through intelligent customer flow management.