Posizone

Posizone is a conceptual framework for representing space in location-based systems by partitioning environments into discrete zones with associated positional uncertainty. It is used to manage imprecision from sensors and signals by treating position as a probability distribution over zones rather than a single point. Each zone has attributes such as center coordinates, radius, and probability mass, which can be updated as data arrives from sensors, beacons, maps, or user input. The framework supports transitions between zones, allowing applications to infer likely movements and plan routes at a coarser granularity. It is compatible with indoor and outdoor settings and can integrate with GIS data, floor plans, and routing algorithms.

Applications include indoor navigation, asset tracking, robotics, and augmented reality, where exact coordinates may be unreliable or unnecessary. By working with zones, posizone can improve privacy by avoiding fine-grained location disclosure and reduce computational load for real-time queries. The modeling approach can be implemented with probabilistic data structures or grid-based representations and may incorporate machine learning to update zone parameters based on historical data. Limitations include potential loss of precision, calibration needs for zone definitions, and the complexity of combining multiple data sources. The concept aligns with broader trends in probabilistic positioning, geofencing, and zone-based routing.