Balancing Battery and Precision: My Journey Building a Geofencing Engine
It happened during a quiet Friday sermon at the mosque. The room was hushed, filled with the soft hum of devotion, when a piercingly loud notification chime erupted from a phone in the front row. The owner scrambled to silence it, visibly flustered, his face turning beet red as heads turned in irritation. That moment wasn't just a nuisance; it was a profound social disruption. I sat there…
In a mosque, a loud phone notification caused disruption, leading the reporter to ponder why smartphones fail to manage notifications appropriately. This frustration birthed the idea for Muffle, a geofencing engine designed to silence phones automatically when in specific locations. Existing solutions either use time-based schedules or require manual intervention, neither of which align with the reporter's goal of a fully automated system.
The reporter tackled the challenge of balancing location accuracy and battery health. Android's GeofencingClient simplifies the process but introduces inconsistencies. A fixed 50-meter radius led to poor performance due to the location provider's reliance on Wi-Fi, Bluetooth, and cellular triangulation. To improve accuracy, the reporter implemented a hybrid system, combining GeofencingClient and FusedLocationProviderClient for secondary verification. This approach keeps the app in background mode, avoiding battery drain.
However, the reporter faced challenges with ACCESS_FINE_LOCATION permissions across Android devices. A custom heartbeat check in a Room database helped detect when the OS restricts location monitoring, prompting notifications to users. For future development, the reporter suggests using a learned model that combines Wi-Fi SSIDs with GPS coordinates, improving accuracy without excessive battery usage.
They also advise against relying on a single sensor type, as GPS indoors is unreliable, potentially driving users to uninstall the app.
Managing overlapping routines added another layer of complexity. The reporter created a priority queue to handle conflicts, pausing routines temporarily when manually overridden. This decision prioritizes user intent over algorithmic convenience. The key takeaway for developers is to respect users' hardware, accepting that location systems are approximations. Building architecture around this understanding leads to more robust and user-friendly location-based automation.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
