Smart building mendix azure influx / smart City / IoT
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This document discusses implementing a smart buildings solution using IoT sensors, Azure cloud services, and open source technologies. The solution aims to increase building usage satisfaction and utilization while reducing energy and cleaning costs. It generates insights from sensor data to trigger actions like notifications. It considers using Mendix for a digital twin and visualizations, InfluxDB as a high performance time series database, and the Azure IoT platform or open source TICK stack for ingesting and analyzing IoT data. The document evaluates these options and shows a demo implementation of a smart building monitoring dashboard.
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Smart building mendix azure influx / smart City / IoT
- 1. Smart Buildings Actionable Data in Mendix with Azure & Open Source 1Smart Buildings Gertjan van het Hof IoT Solution Architect IoT Enthusiast
- 2. Smart Building Use Case Goals: • Increase building usage satisfaction. • Increase building utilization. • Reduce building energy footprint. • Reduce cleaning costs. 2Smart Buildings
- 3. Things Generate data Insights Based on Data Generated Actions Based on Insights The concept how to achief this 3Smart Buildings
- 4. Actionable Insights • Notification of alerts/alarms • Alerts/Alarms with severity level • Functionality to adjust severity level by end-user • Functionality to add user comments • Rich visualizations & KPI • Functionality to drill down to sensor readings • Digital Twin 4Smart Buildings
- 5. Things Generate data Insights Based on Data Generated Actions Based on Insights Smart Building Solution Sensors LoRa Gateway Cloud Platform 5Smart Buildings
- 6. Design Decisions • Digital Twin in Mendix. • Visualizations of telemetry data with Mendix Charts / Plotly. • No telemetry data store to Mendix. • Aggregated data and Alerts/Alarms exposed by APIs. • High Performance (Query) Database. • Real-time anomaly detection. • LoRa sensors and The Things Network. 6Smart Buildings
- 7. Sensor used in this use case • LoRa Alliance Certified • Temperature • Humidity • Light • Occupancy (body sense) • NFC for easy configuration • Over the air configuration • Size : 86x86x26mm • Accuracy: ± 0.5°C, ±2%rh • Resolution: 0.1°C, 0.1%rh • Approx. range: 8km* • Battery life: 5 years** • US902-928, EU863-870, AS923, AU915-928, KR920-923 • 2 x 3.6V AA lithium battery LoRa ERS Eye from ELSYS 7Smart Buildings
- 8. Two alternative implementations for IoT Foundation VS “Open Source”Closed Source PaaS 8Smart Buildings
- 9. What type of database for sensor data do we need? • The data is (mostly) recorded as a new entry. • The data usually arrives in time order. • Time is a primary axis (time- intervals can be either regular or irregular). • High number of sensor readings. 9Smart Buildings
- 10. InfluxDB no 1 Time Series DB 10Smart Buildings
- 11. InfluxDB vs MongoDB • InfluxDB outperformed MongoDB by 2.4x when it came to data ingestion. • InfluxDB outperformed MongoDB by delivering 20x better compression. • InfluxDB outperformed MongoDB by delivering 5.7x better query performance. 11Smart Buildings
- 12. Azure IoT Solution 12Smart Buildings IoT Hub Streaming Analytics CosmosDB Function Apps API Gateway Machine Learning Anomaly Detection & Data Aggregation Cloud Entry Point Alerts & Data Telemetry Data Store API’sDevice Management
- 13. Influxdata Solution 13Smart Buildings API Gateway Influxdata TICK stack API Gateway Machine Learning TensorFlow underneath Cloud Entry Point Device Management API’s
- 14. Influxdata TICK Stack 14Smart Buildings Data StorageIngestion Steaming Analytics Ops Dashboard HTTP
- 15. AMIS Meeting Room Demo Implementation Sensors LoRa Gateway 15Smart Buildings
- 18. Dashboard Climate & Utilization Conclusion (Nieuwegein / Heerlen coming soon) 18Smart Buildings
- 19. Smart Building / Climate / Occupancy 6 19Smart Buildings
- 20. Smart Building / Climate / Occupancy 20Smart Buildings
- 21. Smart Building / Climate / Occupancy 21Smart Buildings
- 22. Takeaways • Community Edition of most Open Sources products is single node. • Implementation speed with Azure Services is higher compared to Open Source alternative. • Azure ML is closed environment. Alternative is Azure Data Bricks, but this is a complete different architecture and much more complex. • With Influxdata TICK stack + Loud ML a cost effective solution can be build. • For advanced ML Use Cases use TensorFlow with Jupiter Notebook. • Mendix can be used for making data actionable but complex visualizations can be problematic. 22Smart Buildings
Editor's Notes
- ASB = Anti Social Behaviour
- Internet of Things is het aansluiten van fysieke objecten aan het internet Daarmee vragen we de status op en monitoren we het gebruik Op basis van deze data trekken we conclusies Die resulteteren in acties in het ERP system Of direct ingrijpen op het aparaat zelf