I'm looking for some advice.
I'm developing a system with geographic triggers, these enable my device to perform certain actions depending on where it is. The triggers are contained within polygons that are stored in my database I've explored multiple options to get this working, however, I'm not very familiar with geo-spacial systems.
An option would be to use the current location of the device and query the DB directly to give me all the polygons that contain that point, thus, all the triggers since they are linked together. A potential problem with this approach, I think, would be the possible amount of polygons stored, and the frequency of the queries, since this system serves multiple devices simultaneously and each one of them polls every few seconds.
An other option I'm exploring is to encode the polygons to an array of geo-hashes and then attach the trigger to each one of them.
Green is the geohashes that the trigger will be attached to, yellow are areas that need to be recalculated with a higher precision. The idea is to encode the polygon in the most efficient way down to X precision.
An other optimization I came up with is to only store the intersection of polygons with roads since these devices are only use in motor vehicles.
Doing this enable the device to work offline performing it's own encoding and lookup, with a potential disadvantage being that the device will have to implement logic to stay up-to-date with triggers added or removed ( potentially every 24 hours )
I'm looking for the most efficient way to implement this given some constrains such as:
- Potentially unreliable networks ( the device has LTE connectivity )
- Limited processing power, the devices for now are based on a raspberry pi 3 Compute module, however, they perform other tasks such as image processing.
- Limited storage, since they store videos and images.
- Potential large amount of triggers/polygons
- Potential large amount of devices.
Any thoughts are greatly appreciated.