Dimension Four has supported geolocation data from the early versions of the product, and now we have improved this powerful functionality even further. Previously it was done by treating geolocation data in the same way as other signals (ie temperature etc). 

With this new update we have made it possible to embed location data into the signals. I.e. - if you send a temperature signal, this can contain both the obvious temperature reading, but also the location of where the device was located when the temperature reading actually happened.

This new functionality reduces the complexity of using location data together with other data series, as the number of API requests or if you prefer, the complexity of nested requests, are significantly reduced. 

To highlight the differences of these two approaches, here are two examples of how this looks in real life.

First example is based on how we have done this previously. Here the location data is created as separate signals for longitude and latitude. 

Ex: Acceleration signal, latitude signal and longitude signal

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"node": {
  "timestamp": "2023-01-22T12:54:16.000Z",
  "type": "ACCEL",
  "unit": "METERS_PER_SECOND_SQUARED",
  "data": {
      "numericValue": 0.8646020889282227
  }
  "node": {
    "timestamp": "2023-01-22T12:54:16.000Z",
    "type": "latitude",
    "unit": "LATITUDE_DEGREES",
    "data": {
      "numericValue": 59.83888197875977
    }
  }
}

"node": {
  "timestamp": "2023-01-22T12:54:16.000Z",
  "type": "longitude",
  "unit": "LONGITUDE_DEGREES",
  "data": {
    "numericValue": 10.43433475494385
  }
}

In this second example we show you how you now also can choose to embed location data into a signal (temperature or other).

Ex: Acceleration signal with location embedded

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"node": {
  "timestamp": "2023-01-22T12:54:16.000Z",
  "type": "ACCEL",
  "unit": "METERS_PER_SECOND_SQUARED",
  "location": {
    "lat": 59.833621974449766,
    "lon": 10.434334754943848
  },
  "data": {
    "numericValue": 0.8646020889282227
  }
}

Geolocation can be used to track the location of connected devices and create contextual user experiences. For example, developers can use geolocation data to automatically adjust the settings of a device based on its location. This could make it easier for users to adjust settings or access content without having to manually change them every time they move. 

Geolocation data can also be used to improve the accuracy of analytics and insights. By tracking the location of connected devices, companies can gain a better understanding of how customers interact with their products, allowing them to make better decisions and more informed product updates. In addition to these benefits, geolocation data can also be used to improve safety by alerting users if a connected device has been moved beyond a certain distance; i.e. geofencing. This can be useful for a range of products, including drones or autonomous vehicles, where precise location data can be used to ensure safety and compliance. 

Further, in fleet management, geofencing can be used to help companies keep track of their vehicles and drivers. By setting up a virtual fence around an area, a fleet manager can be alerted when a vehicle enters or exits the designated area. This allows them to monitor and control the organization’s resources and ensure that their vehicles are being used in a safe and efficient manner. It can also be used to track fleet performance, such as fuel consumption, average speed, and other important metrics. By monitoring these metrics, fleet managers can optimize their operations and ensure that their vehicles are being used as efficiently as possible.

Taking the interoperability of Dimension Four, where you can use one API towards multiple types of sensors, independent of vendor, you can also reap the benefits of combining sensors. For example in regards to preventative maintenance where you could have one sensor for position and one for measuring the condition of a technical part, whether it be in a factory, a vehicle or a remote plant. This setup can also generate valuable insight through aggregated data.

Finally, all the vast IoT data accumulated can be visualized through time series, providing a powerful way of viewing big data in a comprehensive way. And naturally, this data can be taken further for machine learning purposes to strengthen the analysis and build an even stronger case when it comes to for example preventative maintenance.   

For information on how to use this functionality, head over to the developer docs.

Curious about the IoT platform and would like to discuss how we help you on your project, reach out!