Using Sensor Information for Inferring and Forecasting Large-Scale Phenomena

• US 20150379408A1
• Filed: 06/30/2014
• Published: 12/31/2015
• Est. Priority Date: 06/30/2014
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Kapoor, Ashish, Horvitz, Eric
• Application Number:
  US14/320,089
• Publication Number:
  US 20150379408A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01W 1/10 Devices for predicting weather conditions computers per se G06; display devices G09

Abstract

Various techniques for inference and prediction about large-scale phenomena from sensors are described herein. A system includes a processor to execute processor executable code, wherein the processor executable code, when executed by the processor, causes the processor to combine sensor data from a plurality of sensors embedded in moving objects. The code causes the processor to process the combined data using a spatial statistics model. The code also enables the processor to infer an unobserved condition or to predict a future condition based on the processed data.

Claims

1. A system for prediction, comprising a processor to execute processor executable code, wherein the processor executable code, when executed by the processor, causes the processor to:

combine sensor data from a plurality of sensors embedded in moving objects;

process the combined data using a spatial statistics model; and

predict a future condition based on the processed data.

2. The system of claim 1, the combined data further comprising data from a stationary network.

3. The system of claim 2, wherein the stationary network comprises a plurality of wind sensors and/or water current sensors.

4. The system of claim 1, wherein the spatial statistics model comprises a Gaussian process model.

5. The system of claim 1, wherein the moving objects comprise a plurality of aircraft and the sensor data comprises groundspeeds and airspeeds of the aircraft.

6. The system of claim 1, wherein processing the combined data comprises detecting a value for missing data, the value for the missing data calculated by using the spatial statistics model and the sensor data from at least one of the plurality of sensors.

7. The system of claim 1, further comprising processor-executable code, wherein the processor-executable code, when executed by the processor, causes the processor to calculate an expected value of information.

8. The system of claim 7, further comprising processor-executable code, wherein the processor-executable code, when executed by the processor, causes the processor to request and receive additional sensor data based on the expected value of information and to revise the future condition based on the additional sensor data.

9. The system of claim 7, further comprising processor-executable code, wherein the processor executable-code, when executed by the processor, causes the processor to cause one or more aircraft to change a flight plan based on the expected value of information.

10. A method for processing sensor data from a plurality of moving objects, comprising:

receiving the sensor data from the plurality of moving objects;

combining the sensor data; and

processing the combined data using a spatial statistics model, including calculating a value for missing information based on the combined data.

11. The method of claim 10, the combined data further comprising data from a stationary network.

12. The method of claim 11, wherein the stationary network comprises a plurality of wind sensors and/or water current sensors.

13. The method of claim 10, further comprising predicting a future condition based on the combined data and the value for the missing information.

14. The method of claim 13, further comprising:

determining an expected value of information that indicates a frequency at which sensor data is to be collected; and

requesting additional sensor data from a location based on the frequency indicated by the expected value of information.

15. The method of claim 13, further comprising determining an expected value of information based on weighting factors and moving a subset of the plurality of moving objects based on the expected value of information, the weighting factors comprising current and future scheduled routes, priority of the moving objects, and potential benefits from improved prediction.

16. The method of claim 10, comprising using the processed data to generate a route for air transportation.

17. The method of claim 16, the air transportation comprising a glider or balloon that uses wind for flight.

18. The method of claim 10, comprising using the processed data to generate a route for ground transportation.

19. The method of claim 10, comprising using the processed data to generate a route for water transportation, the sensor data comprising wind data and water current data.

20. The method of claim 10, comprising using the processed data to detect turbulence and predict future turbulent regions.

21. The method of claim 10, comprising using the processed data to control the positioning or configuration of turbines, the turbines comprising windpower turbines and/or turbines used in gyre-based power generation.

22. The method of claim 10, comprising using the processed data to forecast weather conditions.

23. The method of claim 10, comprising using the processed data to determine a route for the moving objects to travel based on a future condition and an expected value of information, wherein the moving objects are controlled via onboard or distributed controls.

24. One or more computer-readable storage media for analysis of sensor data, comprising a plurality of instructions that, when executed by a processor, cause the processor to:

detect access data that indicates a cost of collecting sensor data from a plurality of moving objects;

request the sensor data from a subset of the plurality of moving objects based on the access data;

combine the sensor data with data from a stationary network;

process the combined data using a spatial statistics model; and

predict a future condition based on the processed data.

25. The one or more computer-readable storage media of claim 24, wherein the stationary network comprises a plurality of wind sensors and/or water current sensors.