An Interoperable Architecture for the Internet of COVID-19 Things (IoCT) Using Open Geospatial Standards-Case Study:...

Area of Science:

• Geospatial Information Science
• Computer Science
• Public Health

Background:

• The COVID-19 pandemic necessitates integrated sensing solutions for real-time workplace safety.
• Existing Internet of Things (IoT) solutions for COVID-19 lack interoperability, hindering data exchange and informed decision-making.
• Disparate data silos prevent effective information utilization for industry reopening and workforce protection.

Purpose of the Study:

• To design and develop an interoperable Internet of COVID-19 Things (IoCT) architecture.
• To enable the exchange, aggregation, and reuse of disparate IoT sensor data for real-time risk assessment.
• To support informed decision-making for workplace safety and reopening strategies.

Main Methods:

• Developed an IoCT architecture based on the Sensor Web paradigm and an indoor geospatial data model.
• Integrated Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) and IndoorGML standards.
• Implemented a real-world case study for workplace reopening, including contact tracing, people density, and risky behavior monitoring.

Main Results:

• Demonstrated the first real-world integrated implementation of OGC SWE and IndoorGML for online COVID-19 risk calculation.
• The IoCT architecture provides an open standard-based information model, architecture, and tools for interoperability.
• Achieved finer spatial-temporal granularity in monitoring disparate COVID-19 systems.

Conclusions:

• The proposed IoCT architecture effectively integrates heterogeneous IoT systems for enhanced workplace safety.
• The system enables real-time risk assessment based on person-to-place interactions, facilitating informed decisions.
• The developed solution supports safe workplace operations and reopening strategies during and after the pandemic.