IoTMapper: A Metrics Aggregation System Architecture in Support of Smart City Solutions

Area of Science:

• Computer Science
• Urban Planning
• Telecommunications Engineering

Background:

• Smart cities rely on sensor networks and software platforms for management.
• Low-Power Wide-Area Networks (LPWAN) are crucial for urban sensorization.
• Urban environments challenge LPWAN signal propagation, necessitating network monitoring.

Purpose of the Study:

• To present an architecture for aggregating metrics from heterogeneous LPWAN networks.
• To address the limitation of existing solutions monitoring only single LPWAN types.
• To develop a scalable and adaptable solution for smart city network assessment.

Main Methods:

• Developed the IoTMapper architecture integrating custom components with FIWARE and Apache Kafka.
• Utilized adapters to abstract LPWAN implementation details for easy integration of new networks.
• Validated the architecture using real-world Long-Range Wide-Area Network (LoRaWAN) data from Lisbon and simulated data.

Main Results:

• The IoTMapper architecture is a viable solution for aggregating metrics from multiple LPWAN types.
• The architecture demonstrates potential for expansion to support diverse network technologies.
• Performance bottlenecks were identified in certain FIWARE components, impacting scalability.

Conclusions:

• The proposed IoTMapper architecture effectively aggregates metrics from heterogeneous LPWAN networks.
• The modular design allows for the integration of new LPWAN technologies.
• Addressing FIWARE component performance is crucial for scaling the solution in dense smart city environments.