It happened during a quiet afternoon at the local library. I was deep in a focused work session when suddenly, the rhythmic hum of the room was shattered by my ringtone—an upbeat, default alert that seemed to echo off every wall. The stares were instant, heavy, and entirely earned. I fumbled to silence the device, but the damage was done. I had missed my routine meeting silence trigger, and my phone, completely unaware of its social context, had decided that was the perfect moment to announce an incoming email. It was a humiliating, avoidable, and deeply frustrating moment.
We have all experienced this friction. Whether it is a mosque during prayer, a classroom, or a high-stakes interview, our phones are rarely as context-aware as we need them to be. The standard solution is manual intervention: reaching into a pocket, toggling volume keys, or digging into Quick Settings. But humans are forgetful. We silence the phone for a meeting and then go three hours wondering why we missed urgent calls afterward. I wanted a solution that didn't require me to touch my phone, but I was terrified of the common trade-off: battery drain. Most apps that track location rely on constant GPS polling, which effectively turns your phone into a space heater that dies by lunch.
To build Muffle, I knew I had to move away from the naive approach of 'get current location every X minutes.' That is a death sentence for battery life and a nightmare for background execution stability on modern Android versions. Instead, I architected a solution using the GeofencingClient API combined with a localized, offline-first approach to trigger management. The core decision was to offload the heavy lifting to the Google Play Services geofencing engine rather than rolling my own location listener. By creating circular geofences with specific transitionTypes—specifically GEOFENCE_TRANSITION_ENTER and GEOFENCE_TRANSITION_EXIT—I let the OS handle the hardware-level monitoring.
kotlin
val geofence = Geofence.Builder()
.setRequestId(routineId)
.setCircularRegion(lat, lon, radius)
.setExpirationDuration(Geofence.NEVER_EXPIRE)
.setTransitionTypes(Geofence.GEOFENCE_TRANSITION_ENTER or Geofence.GEOFENCE_TRANSITION_EXIT)
.build()
This architecture works because the OS uses a combination of network triangulation and low-power hardware sensors to detect proximity, keeping the main CPU asleep until a boundary is actually crossed. Once a transition occurs, the PendingIntent triggers a BroadcastReceiver, which then checks my local Room database to determine the appropriate sound profile to apply. This keeps the app entirely offline; there is no cloud lookup, no tracking, and no latency. The AudioManager service then handles the state transition, ensuring that even if the screen is off, the volume profile snaps to the desired state in milliseconds. I prioritize these events using a custom priority queue logic within the local database to handle overlapping routines, ensuring that the most restrictive sound profile always wins.
What surprised me most during development was the volatility of GPS accuracy in dense urban environments. My initial assumption was that a 50-meter radius would be the sweet spot for triggering a 'Silent' profile when entering a building. I was wrong. In high-density areas with tall buildings, 'GPS drift' caused the device to think I had exited and re-entered the geofence while I was sitting perfectly still in a chair. This resulted in the phone rapidly toggling between 'Silent' and 'Normal' modes, which was not only noisy but also a massive battery drain as it spiked the radio usage. I had to implement a 'dwell time' requirement—the geofence trigger only fires if the user remains within the boundary for a minimum of 30 seconds. This simple addition killed the flickering effect entirely and saved the battery from unnecessary state-change cycles.
Another painful lesson was how Android handles foreground services. I initially thought I could just run a background worker, but Android’s 'Doze' mode would ruthlessly kill my process to save power. I had to transition to a ForegroundService with a sticky notification to keep the geofencing engine alive. This felt invasive, but it is the only way to ensure the app functions reliably. If I were to start over, I would put more effort into batching location updates instead of relying purely on event-based triggers, as this would provide even more resilience against signal loss in basements or windowless rooms. Relying on a single API is never enough; you need a fallback mechanism that checks the device's state periodically if the geofence event fails to fire.
Ultimately, the lesson here is that automation is only as good as its reliability. If a feature is supposed to make your life easier but requires constant manual checking to see if it worked, it has failed. The key to building a low-power, background-heavy utility is not to fight the Android OS, but to leverage the hardware-level APIs that are designed for low-power operation, such as the GeofencingClient and AlarmManager. By keeping the logic local and minimizing the frequency of high-power operations, you can build tools that feel invisible. I built Muffle to solve my own need for silence during my daily routine, and I hope this approach to geofencing helps you build more efficient background processes in your own projects. You can see how I implemented the rest of the logic or try the app yourself at https://play.google.com/store/apps/details?id=com.muffle.app. It is a work in progress, and I am still iterating on how to handle edge cases in rural areas, but the current engine has proven that you don't need a cloud backend to make a phone truly smart.
