With all due respect, 170M points is a trivial data model, it fits on a thumb drive. That is seconds worth of data in IoT; you can brute-force the implementation and look great. Several well-known companies do. It just doesn't scale to anything real.
The location analytics data models I need to support, e.g. mobile telecom, have trillions of polygons. Indexing millions of records per second continuously concurrent with sub-second queries is pretty normal for IoT, and easy to support with good computer science. (Location is almost never described as a point by primary sources but as polygons; these are converted to centroid approximations for platforms incapable of doing analysis on the source.)
While I do not know the details of your implementation, I do know that indexing complex geometries using a skiplist index won't achieve the necessary volume or velocity required for IoT-like applications. If you restrict yourself to point data, people have been building extremely fast, extremely large quad-trees for years; you just can't scale high-value analytics with them, which limits their utility.
Fair enough; though I'm intrigued by the mention of trillions of polygons. How complex are they? 10 points? 100? 1000? What benefit does the raw data give that centroids can't?
The location analytics data models I need to support, e.g. mobile telecom, have trillions of polygons. Indexing millions of records per second continuously concurrent with sub-second queries is pretty normal for IoT, and easy to support with good computer science. (Location is almost never described as a point by primary sources but as polygons; these are converted to centroid approximations for platforms incapable of doing analysis on the source.)
While I do not know the details of your implementation, I do know that indexing complex geometries using a skiplist index won't achieve the necessary volume or velocity required for IoT-like applications. If you restrict yourself to point data, people have been building extremely fast, extremely large quad-trees for years; you just can't scale high-value analytics with them, which limits their utility.