Integration and Exploitation of Sensor Data in Smart Cities through Event-Driven Applications.

Area of Science:

• Computer Science
• Urban Planning
• Data Science

Background:

• Smart cities utilize Internet of Things (IoT) devices for urban data, but integrating and analyzing this data, especially spatial information, is challenging.
• Existing methods offer limited spatial data analysis capabilities, hindering effective decision-making in smart city applications.

Purpose of the Study:

• To propose a generic approach for integrating spatiotemporal capabilities into smart city information services.
• To develop a reference architecture for event-driven applications that process IoT data in real-time.

Main Methods:

• A multidisciplinary approach was adopted for data integration and real-time processing.
• A reference architecture for event-driven applications was developed, integrating IoT devices, complex event processing, and spatiotemporal analytics.
• A processing workflow was designed for the detection of geographic events.

Main Results:

• A system prototype was implemented and tested on an existing sensor network.
• The feasibility and performance of event-driven applications for real-time processing were demonstrated.
• The scalability of the proposed approach for detecting geographic events was validated.

Conclusions:

• Event-driven applications offer a viable solution for real-time processing and geographic event detection in smart cities.
• The developed reference architecture facilitates the seamless integration of IoT data and spatiotemporal analytics.
• This approach enhances the utility of IoT data for informed urban planning and management.