Real-time sensor data aggregation for ponds/tanks

Purpose

1.1. Aggregate real-time sensor data (temperature, pH, dissolved oxygen, ammonia, water level, turbidity, salinity, feed dispenser status) from all pond and tank sensors for instant monitoring and rapid anomaly detection.
1.2. Centralize sensor streams to enable predictive analysis, automated alerts, equipment control, compliance logging, and remote troubleshooting.
1.3. Standardize protocols (MQTT, HTTP/S, Modbus) to support multi-brand sensor deployments, with data normalization for easy dashboard visualization and analytics integrations.

Trigger Conditions

2.1. New reading posted by any in-pond/tank IoT sensor.
2.2. Predefined time interval reached (e.g. every 60 seconds for periodic polling).
2.3. Sensor error or device reconnect event detected.
2.4. External API push from equipment controller or farm’s upstream ERP.

Platform Variants

3.1. AWS IoT Core
• Feature: Device data ingestion pipeline; configure "Rule Actions" for direct payload streaming.
• Sample: Attach MQTT topic filter to “pond-sensor/#”, route results to Kinesis/DataLake.

3.2. Microsoft Azure IoT Hub
• Feature: Device-to-cloud message routing; configure Event Grid integration.
• Sample: Device registry for all tanks, message routing to Azure Functions for parsing.

3.3. Google Cloud IoT Core
• Feature: MQTT/HTTP Bridge; set up Pub/Sub triggers.
• Sample: Pub/Sub topic per pond, subscribe cloud function for processing.

3.4. ThingSpeak
• Feature: Channel feeds/API keys for data writes.
• Sample: POST pH and temp values to channel field API.

3.5. Losant
• Feature: Edge workflows; device state triggers.
• Sample: Set workflow: “sensor data received” → dashboard update.

3.6. UbiDots
• Feature: Device Variables & Events; Webhook integration.
• Sample: Each sensor stream mapped as variable, with webhook alert if value out of range.

3.7. Particle Cloud
• Feature: Device event publish/subscribe; Webhook outbound.
• Sample: Publish “tempReading” event; route to farm database via webhook.

3.8. Adafruit IO
• Feature: Feed API and MQTT telemetry.
• Sample: Configure “pond-tank/oxygen” feed, subscribe to MQTT for dashboard.

3.9. Datacake
• Feature: Device management; Data API connection.
• Sample: LoRaWAN device auto-report, trigger webhook on new data.

3.10. Home Assistant
• Feature: MQTT integration and Automation Rules.
• Sample: Automation: on “tank_1_mqttpump” state change, update entity/state.

3.11. OpenRemote
• Feature: Data sources and Asset attribute bindings.
• Sample: Map sensor endpoints onto asset tree for real-time sync.

3.12. Node-RED
• Feature: MQTT/HTTP-in nodes; custom flows.
• Sample: “Input” node for all sensor MQTT topics, flow splits per pond.

3.13. InfluxDB
• Feature: Time series ingestion endpoint.
• Sample: “Write API” to push each timestamped sensor value.

3.14. Grafana Cloud
• Feature: Data Source integration and Alerting.
• Sample: Ingest via InfluxDB, set “Alert Rules” for thresholds.

3.15. Salesforce Platform Events
• Feature: Real-time event API.
• Sample: Push sensor anomaly as Platform Event to farm management.

3.16. Zapier
• Feature: Webhook & Scheduler triggers.
• Sample: Webhook “catch” for pond data, push to Google Sheets/Slack.

3.17. Pipedream
• Feature: Custom workflow triggers with webhook/IoT.
• Sample: Webhook endpoint receives sensor data, passes to downstream API.

3.18. Webhooks (Generic)
• Feature: Raw HTTP/S POST endpoint for sensor firmware.
• Sample: Every reading auto-POST to specified automation endpoint.

3.19. MongoDB Realm
• Feature: Trigger on Data Insert.
• Sample: Insert “pondReading” document, auto-trigger backend function.

3.20. Arduino IoT Cloud
• Feature: Thing properties & dashboard sync via API.
• Sample: Device property update triggers cloud rule for notifications.

3.21. Domoticz
• Feature: Hardware/Sensor integration, HTTP/MQTT import.
• Sample: Periodically poll and store water-level readings.

3.22. IFTTT
• Feature: Webhook “Receive a web request” as trigger.
• Sample: POST endpoint for every new data point, triggers notification applet.

Benefits

4.1. Provides immediate farm-wide visibility of evolving aquaculture water quality metrics.
4.2. Enables predictive equipment maintenance and livestock health forecasting.
4.3. Minimizes manual errors with digital records of all pond/tank conditions.
4.4. Rapid anomaly detection lowers stock stress and death risk.
4.5. Lowers compliance risk with automated, timestamped data logs for regulations.
4.6. Supports integration with IoT, dashboard, reporting, and alerting tools of choice.
4.7. Streamlines expansion to new sensors, hardware, or sites with modular automation endpoints.