Smart Buildings Actionable Data in Mendix with Azure & Open Source

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