PinMe Location Tracking Without GPS Access
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Solution Overview
Problem
Existing location tracking methods for mobile devices require initial GPS coordinates, prior knowledge of travel routes, and high sampling rates of sensory data, which limits their effectiveness and raises privacy concerns, as they often rely on continuous data collection and specific training datasets, making them inefficient and suspicious.
Innovation Solution
The PinMe system determines the last wireless network connection, compiles publicly available auxiliary information, and classifies user activities to estimate location using sensory and non-sensory data without GPS access, allowing for accurate tracking across various activities like driving, flying, or walking, using a low sampling rate and without prior knowledge of routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous high sampling rate data collection is used for location tracking, then measurement precision is improved, but device complexity and detection risk increase
Solution Approach 1:
The patent applies periodic action by using event-triggered sampling instead of continuous high-rate sampling. The system collects sensory data at variable intervals based on activity state changes detected from accelerometer patterns, power consumption variations, and other indicators. This allows achieving comparable location tracking accuracy while significantly reducing the complexity of continuous data collection and lowering detection risk.
2Measurement precision
If prior knowledge of travel routes and initial GPS coordinates is required, then measurement precision is improved, but adaptability deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-processing and storing mobility patterns, power consumption characteristics, and environmental data associations in training datasets before actual tracking. This allows the system to achieve accurate location estimation without requiring real-time prior knowledge of routes or initial GPS coordinates, thereby improving adaptability to unknown locations and routes while maintaining measurement precision.
Solution Approach 2:
The patent uses copying by creating and storing training datasets that capture typical mobility patterns, sensor readings, and location associations. These copied patterns from training data enable the system to estimate locations accurately without needing actual GPS or prior route knowledge during operation, enhancing adaptability while maintaining precision.
3Measurement precision
If GPS location services are accessed, then measurement precision is improved, but privacy protection deteriorates
Solution Approach 1:
The patent applies taking out by extracting location information indirectly through sensory data analysis rather than directly accessing GPS location services. The system processes accelerometer readings, barometer data, magnetometer information, and power consumption patterns to infer location and movement patterns, thereby achieving comparable accuracy while eliminating the privacy risks associated with direct GPS access and location service permissions.
4Measurement precision
If specific training datasets and prior route knowledge are required, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent applies preliminary action by performing offline training to create mobility pattern datasets before deployment. This pre-processing step captures typical sensor readings, power consumption patterns, and location associations, which are then stored for use during actual tracking operations. This approach enables accurate location estimation without requiring operators to input route knowledge or configure complex parameters during operation, significantly improving ease of deployment and operation.
Data Source
AI summary
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.