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Construction Improvements

After the MVP was completed, the next step was to improve the overall work pipeline, complete other use cases, improve the overall system and use the real sensor data we are provided.

Work Pipeline Improvements​

We started to improve the overall work, by working on branches acording to the feature being developed, and using pull requests to ask merges to main. The pull request needs to be reviewed and approved by at least 2 other developers and, on the backend repository, the code needs to pass github actions tests, to ensure security and that all our use cases are still validated.

Use Case Development​

These use cases were developed:

Important Note: Navigation related features on these use cases were still not developed, as they have a lower priority for this project, and they will recieve more focus later on.

Real data Implementation​

Overview​

We have implemented real data integration into our system. We gained access to Weather Stations data and began validating the accuracy of sensor readings against external data sources (Weather API). Additionally, we received the data structure specifications the vehicle systems too, which will allow us to integrate real vehicle data soon.

Weather Stations Data Structure​

{
  "thingId": "meteo:1210702",
  "policyId": "meteo:default",
  "attributes": {
    "id": 1210702,
    "location": {
      "longitude": -8.6596,
      "latitude": 40.6353
    },
    "location_name": "Aveiro (Universidade)"
  },
  "features": {
    "meteorology": {
      "properties": {
        "wind_intensity": 15.5,
        "temperature": 13.5,
        "radiation": 1530.4,
        "wind_direction": 0,
        "accumulated_precipitation": 0,
        "pressure": 1028.9,
        "humidity": 63
      }
    }
  }
}

Vehicle Data Structure​

{
  "thingId": "tolls:lider-5",
  "policyId": "dt4mob:default",
  "attributes": {
    "objectId": 5,
    "expiry_ts": "2026-03-02T19:13:00.478986Z"
  },
  "features": {
    "State": {
      "properties": {
        "id": "5",
        "ts": "2026-03-02T19:12:30.478697Z",
        "extra": {
          "timeOfMeasurement": "2025-11-25T10:55:46.988086Z",
          "speedKmh": 2,
          "localCoordinates": {
            "x": 1.66258697213839673,
            "y": 23.5941424628173838
          },
          "absoluteCoordinates": {
            "latitude": 38.7111805614115095,
            "longitude": -9.38936852690132690
          },
          "dimensions": {
            "width": 1.902,
            "length": 4.823,
            "height": 1.189
          },
          "objectId": 5,
          "emergency": 0
        }
      }
    }
  }
}

Data Comparison Analysis​

We made a comparison between real Sensor Data and OpenWeather API data, using measurements from the same hour and date:

Aveiro (Universidade)
Station ID: 1210702
Location: (40.6353, -8.6596)

Temperature:    Station: 13.5°C    | OpenWeather: 14.5°C    | Diff: -1.0°C
Humidity:       Station: 63%       | OpenWeather: 78%       | Diff: -15%
Pressure:       Station: 1028.9hPa | OpenWeather: 1026.0hPa | Diff: +2.9hPa
Wind Speed:     Station: 15.5km/h  | OpenWeather: 10.6km/h  | Diff: +4.9km/h

Frontend Implementation​

We integrated the real sensor data into the frontend application. Users can now view weather conditions in the app using data from both the local weather sensors or using the OpenWeather API: Frontend Weather Display

Current Status​

We have successfully received and documented the complete data structures for both weather stations and vehicles. Currently, weather station data is fully integrated into our production system (Ditto) and available through the frontend. Vehicle data integration is pending for future development phases.

Tutors:

Group:

Institution: Telecommunications Institute of Aveiro (ITAv)