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Workflow Overview

Workflow

1. React Native Application (Location Sender)

  • Function: Sends real-time GPS location data of bus drivers.
  • Operation:
    • Detects when a bus enters a predefined geofence, signaling the start of a new route.
    • Continuously sends location data to the MQTT broker every second.
    • Detects when the bus re-enters the geofence, marking the end of the route.

2. EMQX (MQTT Broker)

  • Function: Manages real-time MQTT messages from the React Native application.
  • Operation:
    • Receives location data messages.
    • Passes messages to the Data Ingestion Microservice.

3. Data Ingestion Microservice (Python-based)

  • Function: Processes incoming MQTT messages and stores them in MongoDB.
  • Operation:
    • Subscribes to the MQTT topic to receive location data.
    • Determines if a new route needs to be created or if the data belongs to an ongoing trip.
    • Stores the data in the appropriate routes or trips collection in MongoDB.

4. MongoDB Database

  • Collections:
    • routes: Stores static information about each route (path, expected time, etc.).
    • trips: Stores dynamic data about individual trips (start time, end time, actual path, etc.).
  • Operation:
    • New route data is added to the routes collection when a unique route is identified.
    • Trip data is appended to the trips collection, referencing the corresponding route ID.

5. FastAPI Microservice

  • Function: Provides an API for accessing stored route and trip data.
  • Operation:
    • Fetches data from MongoDB upon API requests.
    • Offers endpoints for retrieving route details, trip information, and historical data.

Detailed Steps

  1. Start of Route:

    • The bus driver starts the React Native app upon beginning their route.
    • The app detects entry into the start geofence and begins sending location data to EMQX.

  2. Data Transmission:

    • Location data, including coordinates and timestamps, is sent to EMQX, which forwards it to the Data Ingestion Microservice.

  3. Data Processing and Storage:

    • The Data Ingestion Microservice processes each received message.
    • If a new route is detected (based on geofence entry), a new document is created in the routes collection.
    • Location data is stored in the trips collection, referencing the current route.

  4. End of Route:

    • Upon re-entry into the geofence, the app signals the end of the route.
    • The trip document is updated with the end time and other relevant data.

  5. Data Access and Visualization:

    • The FastAPI Microservice provides endpoints to access route and trip data.
    • This data can be used for real-time monitoring, historical analysis, and route optimization.

  6. Machine Learning and Analytics (Future Scope):

    • Historical route and trip data can be used to train ML models for anomaly detection and route optimization.
    • Regular updates to ML models ensure they adapt to new patterns and changes in route dynamics.

Conclusion

This workflow provides a comprehensive system for real-time GPS route tracking, data processing, storage, and retrieval. It's designed to minimize manual input, automate route and trip data collection, and provide a platform for advanced analytics and optimizations.