Method and apparatus for providing positioning services using multi-spacing clustering

1. A method comprising:

detecting sensor data across a plurality of sensor spaces for a device, wherein the plurality of sensor spaces are associated with respective one or more sensor types;

determining a plurality of sensor signatures per sensor type;

determining a likely intersection point of the plurality of sensor spaces to predict a point propensity for the device and to represent location data of the device based on the sensor data, wherein the likely intersection point matches the sensor signatures; and

performing a clustering of the location data across the plurality of sensor spaces to generate a cluster graph of a physical space associated with the sensor data.

2. A method of claim 1 , further comprising:

performing the clustering using one or more Voronoi polytopes.

3. A method comprising:

detecting sensor data across a plurality of sensor spaces for a device, wherein the plurality of sensor spaces are associated with respective one or more sensor types;

determining a likely intersection point of the plurality of sensor spaces to predict a point propensity for the device and to represent location data of the device based on the sensor data;

performing a clustering of the location data across the plurality of sensor spaces to generate a cluster graph of a physical space associated with the sensor data; and

performing a crowd-sourcing of the location data in combination with other location data determined for one or more other devices within the physical space over one or more time periods.

4. A method of claim 3 , further comprising:

predicting one or more unused spaces within the physical space based on the crowd-sourcing, the location data, the other location data, or a combination thereof.

5. A method of claim 3 , further comprising:

predicting one or more paths within the physical space based on the crowd-sourcing, the location data, the other location data, or a combination thereof.

6. A method of claim 5 , further comprising:

determining a floor plan of the physical space based on the one or more unused spaces, the one or more paths, the cluster graph, or a combination thereof.

7. A method of claim 1 , further comprising:

generating guidance information for navigating within the physical space based on the cluster graph.

8. A method of claim 1 , wherein the one or more sensor types include a magnetometer, a microphone, a camera, a light meter, a short range wireless receiver, or a combination thereof.

9. An apparatus comprising a processor configured to:

detect sensor data across a plurality of sensor spaces for a device, wherein the plurality of sensor spaces are associated with respective one or more sensor types;

determine a plurality of sensor signatures per sensor type;

determine a likely intersection point of the plurality of sensor spaces to predict a point propensity for the device and to represent location data of the device based on the sensor data, wherein the likely intersection point matches the sensor signatures; and

perform a clustering of the location data across the plurality of sensor spaces to generate a cluster graph of a physical space associated with the sensor data.

10. An apparatus of claim 9 , wherein the apparatus is further configured to:

perform the clustering using one or more Voronoi polytopes.

11. An apparatus comprising a processor configured to:

detect sensor data across a plurality of sensor spaces for a device, wherein the plurality of sensor spaces are associated with respective one or more sensor types;

determine a likely intersection point of the plurality of sensor spaces to predict a point propensity for the device and to represent location data of the device based on the sensor data;

perform a clustering of the location data across the plurality of sensor spaces to generate a cluster graph of a physical space associated with the sensor data; and

perform a crowd-sourcing of the location data in combination with other location data determined for one or more other devices within the physical space over one or more time periods.

12. An apparatus of claim 11 , wherein the apparatus is further configured to:

predict one or more unused spaces within the physical space based on the crowd-sourcing, the location data, the other location data, or a combination thereof.

13. An apparatus of claim 11 , wherein the apparatus is further configured to:

predict one or more paths within the physical space based on the crowd-sourcing, the location data, the other location data, or a combination thereof.

14. An apparatus of claim 13 , wherein the apparatus is further configured to:

determine a floor plan of the physical space based on the one or more unused spaces, the one or more paths, the cluster graph, or a combination thereof.

15. An apparatus of claim 9 , wherein the apparatus is further configured to:

generate guidance information for navigating within the physical space based on the cluster graph.

16. A system comprising:

a positioning platform configured to detect sensor data across a plurality of sensor spaces for a device, wherein the plurality of sensor spaces are associated with respective one or more sensor types; to determine a plurality of sensor signatures per sensor type; to determine a likely intersection point of the plurality of sensor spaces to predict a point propensity for the device and to represent location data of the device based on the sensor data, wherein the likely intersection point matches the sensor signatures; and to perform a clustering of the location data across the plurality of sensor spaces to generate a cluster graph of a physical space associated with the sensor data.

17. A system of claim 16 , wherein the positioning platform is further configured to perform the clustering using one or more Voronoi polytopes.

18. A system comprising:

a positioning platform configured to detect sensor data across a plurality of sensor spaces for a device, wherein the plurality of sensor spaces are associated with respective one or more sensor types; to determine a likely intersection point of the plurality of sensor spaces to predict a point propensity for the device and to represent location data of the device based on the sensor data; to perform a clustering of the location data across the plurality of sensor spaces to generate a cluster graph of a physical space associated with the sensor data; and to perform a crowd-sourcing of the location data in combination with other location data determined for one or more other devices within the physical space over one or more time periods.