Considerations while developing a low-power mobile location monitoring app

Considerations while developing a low-power mobile location monitoring app

  • GPS Tracker
  • October 20, 2021
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  • 4 minutes read
  • SOS/panic button and covert gesture for using it without opening the app; near real-time location tracking
  • a conversation that is encrypted from beginning to end;
  • between the mobile device and the server; end-to-end encrypted communication
  • When SOS mode was active, audio recording and streaming were possible.
  • geo-fencing;
  • Notifications by SMS and email;
  • And many other features – see our Portfolio for more information on the application.

The app would not be used if it consumed batteries. Therefore one of the most critical things we were required to achieve was minimal energy consumption. We reached a remarkable result: an average battery consumption of 0.3 percent per hour while continually tracking the user with great accuracy. The average precision of location tracking was roughly 80 meters.

1. Define the term “real-time” correctly.

Even though “real-time” is frequently used, it refers to regular updates at predetermined time intervals rather than real-time. Specificity should be an overarching consideration in any software development process. We updated the user’s location every 180 seconds when moving and every 900 seconds when they were stationary for our algorithm. This was plenty to meet our two objectives: continuous tracking and low power consumption.

2. Create your location tracking and server communication code.

Even if it’s widely used, don’t rely on third-party code when dealing with very particular requirements and performance expectations. We’ve seen you trusted third-party programs act in unexpected ways or have minor flaws that were not properly documented. Another issue with which 3rd party software is not very comfortable is changing specifications. Our advice is to rely on your knowledge, conduct a thorough study, and develop your code.

3. Gain a thorough understanding of the location’s hardware. Allow the hardware to shut down.

We polled the location every 30 seconds when we first started working on the location monitoring module. Everything performed perfectly, although the GPS/location tracking device consumed much power throughout our experiments. We observed that the location tracking-related hardware was maintained turned for 30 seconds after we stopped utilizing it after a few more tests. Because we polled the location every 30 seconds, the location-related hardware was left on all the time, depleting the battery as if it were in use all the time. Our recommendation is to use an algorithm that allows the location tracking hardware to turn off for a short period, hence lowering total battery consumption.

4. Gain a thorough understanding of how network-related hardware functions.

The network had the same issue as the position tracking devices. It was mostly a problem with the cellular data-related hardware, which would remain powered on for an extended period after we had stopped utilizing it. We employed in-memory caching and timed synchronizations with the server to achieve our objectives.

5. Use a stationary mode detection technique and update the location at extended intervals of time.

We determined that tracking the user when stationary at longer time intervals will further reduce battery use in light of our objectives. As a result, we used motion-detecting technology to determine whether or not the user was moving. We only tracked the user’s location every 900 seconds when he was still. The fact that most users were immobile for the majority of the day aided this implementation.

6. Define the position tracking accuracy target.

At first, we tried using cell tower and WiFi location monitoring, but it didn’t deliver the accuracy we needed (the target accuracy was 100 meters). Another factor to consider was that some users would use the app in isolated locations where WiFi service is limited or non-existent. Both the ability to properly find the user and reduce power consumption necessitated the establishment of a location accuracy criterion (when the app gets a location that complies with this requirement, the location polling is stopped).

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