Building Low-Cost Sensing Infrastructure for Air Quality Monitoring in Urban Areas Based on Fog Computing.

Area of Science:

• Environmental Science
• Computer Science
• Sensor Networks

Background:

• Limited public air quality monitoring stations necessitate improved spatial density.
• Integrating numerous low-cost sensors presents significant data volume, velocity, and processing challenges.
• Centralized cloud models struggle with large-scale sensor network management and real-time performance.

Purpose of the Study:

• To propose a methodology and architectural framework for large-scale urban air quality monitoring infrastructure.
• To address the challenges of managing a vast number of sensors and their data.
• To leverage fog computing for efficient, real-time air quality data processing.

Main Methods:

• Developed a tiered architectural solution utilizing fog computing principles.
• Implemented a methodology for managing the entire life cycle of edge-tier sensor nodes (commission, provision, fault detection, recovery).
• Utilized microservices for sensor-side processing encapsulated across different system architecture tiers.

Main Results:

• The proposed fog computing architecture effectively handles large-scale processing requirements.
• The system sustains real-time performance for air quality monitoring applications.
• The methodology for edge-tier node management proved effective in an experimental case study.

Conclusions:

• Fog computing offers a viable solution for large-scale air quality monitoring infrastructure.
• The developed methodology and architecture enhance the management and operation of sensor networks.
• The microservices-based approach facilitates efficient sensor data processing and system collaboration.