Adaptive sensor data selection and sampling based on current and future context

1. A method comprising:

receiving, by a mobile device, raw data from a sensor;

classifying, by the mobile device, the raw data into a context value;

interpreting, by the mobile device, the context value into a higher-level context;

receiving, by the mobile device, a predicted context based on the higher-level context; and

controlling, by the mobile device, the sensor based on the predicted context and a priority framework that indicates priority of control of the sensor between at least two applications running on the mobile device.

2. The method of claim 1 , wherein the controlling includes controlling the sensor based on the higher-level context and the predicted context.

3. The method of claim 1 , wherein the controlling includes controlling at least one of a frequency at which raw data is received from the sensor, or an on/off state of the sensor.

4. The method of claim 3 , wherein the controlling includes adjusting, over time, based on the predicted context, at least one of the frequency at which raw data is received from the sensor, or the on/off state of the sensor.

5. The method of claim 1 , further including:

receiving raw data from a plurality of sensors;

classifying the raw data into a plurality of context values;

interpreting the context values into the higher-level context; and

controlling the sensors based on the higher-level context and the predicted context.

6. The method of claim 5 , wherein the controlling includes controlling at least one of a frequency at which raw data is received from each of the sensors, or a selection of the sensors to be enabled.

7. The method of claim 5 , wherein the controlling includes turning off at least a first one of the sensors and increasing a sampling frequency of at least a second one of the sensors.

8. The method of claim 1 , further including:

sending the higher-level context to a server configured to determine the predicted context from the higher-level context; and

receiving the predicted context from the server.

9. The method of claim 8 , wherein interpreting the context value into the higher-level context is performed by one of the mobile device or the server, based on whether an amount of processing required for the interpreting exceeds a threshold processing limit.

10. The method of claim 1 , wherein the sensor is physically integrated with the mobile device.

11. The method of claim 1 , wherein the sensor is not physically integrated with the mobile device and communicates wirelessly with the mobile device.

12. The method of claim 1 , further comprising:

receiving the priority framework.

13. The method of claim 1 , where the priority framework assigns priority to an affirmative selection of the sensor and a highest sampling frequency of the sensor.

14. A mobile device comprising:

one or more processors to:

receive raw data from sensors;

classify the raw data into context values;

interpret the context values into a higher-level context;

send the higher-level context to a server;

receive a predicted context based on the higher-level context from the server; and

control the sensors based on the predicted context and a priority framework that indicates priority of control of the sensors between at least two applications running on the mobile device.

15. The mobile device of claim 14 , where the one or more processors, when controlling the sensors, are to:

control at least one of a frequency at which raw data is received from each of the sensors, or a selection of the sensors to be enabled.

16. The mobile device of claim 15 , where the one or more processors, when controlling the sensors, are to:

adjust, over time, based on the predicted context, at least one of the frequency at which raw data is received from each of the sensors, or the selection of the sensors to be enabled.

17. The mobile device of claim 14 , wherein at least one of the sensors is physically integrated with the mobile device.

18. The mobile device of claim 17 , wherein at least one of the sensors is not physically integrated with the mobile device and communicates wirelessly with the mobile device.

19. The mobile device of claim 14 , wherein the mobile device is a cell phone.

20. The mobile device of claim 14 , where the one or more processors are further to:

receive the priority framework.

21. A non-transitory computer-readable storage medium, comprising:

one or more instructions that, when executed by one or more processors of a device, cause the one or more processors to:

receive raw data from sensors;

classify the raw data into context values;

interpret the context values into a higher-level context;

receive a predicted context based on the higher-level context; and

control the sensors based on the higher-level context, the predicted context, and a priority framework that indicates priority of control of the sensors between at least two applications running on the device.

22. The non-transitory storage medium of claim 21 , where the one or more instructions, when controlling the sensors, cause the one or more processors to:

control at least one of a frequency at which raw data is received from the sensors, or a selection of the sensors to be enabled.

23. The non-transitory storage medium of claim 21 , where the one or more instructions, when controlling the sensors, cause the one or more processors to:

turn off at least a first one of the sensors and increase a sampling frequency of at least a second one of the sensors.

24. The non-transitory storage medium of claim 21 , the one or more instructions further comprising instructions to:

receive the priority framework.