System and method for tracking a mobile device user
According to various embodiments, a method for locating the user of a mobile device without accessing global position system (GPS) data is disclosed. The method includes determining the last location that the user was connected to a wireless network. The method further includes compiling publicly-available auxiliary information related to the last location. The method additionally includes classifying an activity of the user to driving, traveling on a plane, traveling on a train, or walking. The method also includes estimating the location of the user based on sensory and non-sensory data of the mobile device particular to the activity classification of the user.
1. A method for locating a user of a mobile device comprising:
determining a location that the mobile device was connected to a wireless network based on a network status and internet protocol (IP) address of the mobile device;
compiling publicly-available auxiliary information related to the location the mobile device was connected to the wireless network;
classifying an activity of the user to driving, traveling on a plane, traveling on a train, or walking; and
estimating a current location of the user without accessing global positioning system (GPS) data based on the compiled publicly-available auxiliary information and one or more of sensory data and time zone data of the mobile device particular to the activity classification of the user;
wherein estimating a current location of the user is based on public maps, weather reports, air pressure, acceleration, and heading when the activity of the user is classified as walking.
2. The method of claim 1 , wherein sensory data comprises one or more of air pressure, heading, and acceleration.
3. The method of claim 1 , wherein auxiliary information comprises one or more of public maps, transportation time tables, airport databases, weather reports, and train heading datasets.
4. The method of claim 1 , wherein classifying the activity of the user is based on one or more of machine learning and physical characteristics of each activity.
5. The method of claim 1 , wherein estimating a current location of the user is based on air pressure, heading, one or more public maps, and one or more weather reports when the activity of the user is classified as driving.
6. The method of claim 1 , wherein estimating a current location of the user is based on air pressure, acceleration, time zone, one or more weather reports, one or more airport databases, and one or more flight timetables when the activity of the user is classified as traveling on a plane.
7. The method of claim 1 , wherein estimating a current location of the user is based on acceleration, heading, one or more train timetables, and one or more train heading datasets when the activity of the user is classified as traveling on a train.
8. A system for locating a user of a mobile device comprising a processor programmed to:
determine a location that the mobile device was connected to a wireless network based on a network status and internet protocol (IP) address of the mobile device;
compile publicly-available auxiliary information related to the location the mobile device was connected to the wireless network;
classify an activity of the user to driving, traveling on a plane, traveling on a train, or walking; and
estimate a current location of the user without accessing global positioning system (GPS) data based on the compiled publicly-available auxiliary information and one or more of sensory data and time zone data of the mobile device particular to the activity classification of the user;
wherein estimating a current location of the user is based on public maps, weather reports, air pressure, acceleration, and heading when the activity of the user is classified as walking.
9. The system of claim 8 , wherein sensory data comprises one or more of air pressure, heading, and acceleration.
10. The system of claim 8 , wherein auxiliary information comprises one or more of public maps, transportation time tables, airport databases, weather reports, and train heading datasets.
11. The system of claim 8 , wherein classifying the activity of the user is based on one or more of machine learning and physical characteristics of each activity.
12. The system of claim 8 , wherein estimating a current location of the user is based on air pressure, heading, one or more public maps, and one or more weather reports when the activity of the user is classified as driving.
13. The system of claim 8 , wherein estimating a current location of the user is based on air pressure, acceleration, time zone, one or more weather reports, one or more airport databases, and one or more flight timetables when the activity of the user is classified as traveling on a plane.
14. The system of claim 8 , wherein estimating a current location of the user is based on acceleration, heading, one or more train timetables, and one or more train heading datasets when the activity of the user is classified as traveling on a train.
15. A non-transitory computer-readable medium having stored thereon a computer program for execution by a processor configured to perform a method for locating a user of a mobile device, the method comprising:
determining a location that the mobile device was connected to a wireless network based on a network status and internet protocol (IP) address of the mobile device;
compiling publicly-available auxiliary information related to the location the mobile device was connected to the wireless network;
classifying an activity of the user to driving, traveling on a plane, traveling on a train, or walking; and
estimating a current location of the user without accessing global positioning system (GPS) data based on the compiled publicly-available auxiliary information and one or more of sensory data and time zone data of the mobile device particular to the activity classification of the user;
wherein estimating a current location of the user is based on public maps, weather reports, air pressure, acceleration, and heading when the activity of the user is classified as walking.
16. The computer-readable medium of claim 15 , wherein sensory data comprises one or more of air pressure, heading, and acceleration.
17. The computer-readable medium of claim 15 , wherein auxiliary information comprises one or more of public maps, transportation time tables, airport databases, weather reports, and train heading datasets.
18. The computer-readable medium of claim 15 , wherein classifying the activity of the user is based on one or more of machine learning and physical characteristics of each activity.
19. The computer-readable medium of claim 15 , wherein estimating a current location of the user is based on air pressure, heading, one or more public maps, and one or more weather reports when the activity of the user is classified as driving.
20. The computer-readable medium of claim 15 , wherein estimating a current location of the user is based on air pressure, acceleration, time zone, one or more weather reports, one or more airport databases, and one or more flight timetables when the activity of the user is classified as traveling on a plane.
21. The computer-readable medium of claim 15 , wherein estimating a current location of the user is based on acceleration, heading, one or more train timetables, and one or more train heading datasets when the activity of the user is classified as traveling on a train.