A Service-Oriented Middleware for Integrated Management of Crowdsourced and Sensor Data Streams in Disaster...

Area of Science:

• Environmental Science
• Computer Science
• Data Science

Background:

• Increasing sensor data volume and complexity challenge traditional networks.
• Need for robust data management for diverse applications like disaster response.
• Emergence of "citizens as sensors" as a valuable complementary data source.

Purpose of the Study:

• To design and implement an interoperable middleware for managing spatial, temporal, and thematic sensor data.
• To integrate diverse data streams, including those from citizen sensors, for enhanced data management.
• To improve near-real-time applications through effective sensor data access, publication, and filtering.

Main Methods:

• Developed and deployed reusable middleware utilizing Sensor Web Enablement (SWE) initiative services.
• Integrated a processing engine for managing batch and stream data.
• Conducted interoperability analytics testing with a flood citizen observatory.

Main Results:

• The middleware effectively handles sensor data-stream access, publication, and filtering in dynamic scenarios.
• Demonstrated successful integration of variable crowd-sourced data with sensor data streams.
• Enabled seamless batch and stream management integration for comprehensive data handling.

Conclusions:

• The developed middleware offers an effective solution for managing heterogeneous sensor data, including citizen-generated data.
• The approach enhances public awareness and improves the performance of near-real-time applications, particularly in disaster management.
• Facilitates the integration of diverse data sources, paving the way for more comprehensive environmental monitoring and response systems.