Dynamic Reverse Geofencing for Mobile Object Location
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Solution Overview
Problem
Traditional geofencing techniques rely on accurate and stable GPS locations, which are not always available or reliable, especially in asset tracking applications where large estimation errors and instability in location reporting occur, making it difficult to determine if a mobile object is at its expected location.
Innovation Solution
A system and method that uses cellular network information to generate an estimated location for a mobile object, defines a reverse geofence boundary, and calculates the relative probability of the object's location from a database of known locations, providing a measure of estimation confidence and identifying the most probable location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS-based location determination is used, then location precision is improved, but reliability deteriorates when GPS is unavailable or provides unstable readings
Solution Approach 1:
The patent introduces cellular network location services as an intermediary when GPS is unavailable. The system uses cell tower triangulation and other cellular-based methods to determine location when satellite GPS signals are not available, providing a fallback mechanism that maintains location determination capability under degraded conditions.
Solution Approach 2:
The system dynamically changes the parameters of location determination by switching between GPS-based high-precision mode and cellular network-based mode with larger error margins. When GPS readings become unstable or unavailable, the system transitions to using cellular network information, accepting larger estimation errors in exchange for maintaining continuous location tracking capability.
2Reliability
If cellular network location services are used, then reliability is improved by providing location data when GPS is unavailable, but measurement precision deteriorates due to larger estimation errors
Solution Approach 1:
The patent implements a dynamic location determination system that adjusts its methodology based on available resources and conditions. The system dynamically switches between GPS and cellular network-based location services, and even dynamically adjusts the geofence boundary size based on the estimated error margin of the current location method, optimizing the balance between reliability and precision in real-time.
Solution Approach 2:
The system segments the location determination process into multiple independent methods (GPS-based determination and cellular network-based determination). Each method operates independently with its own error characteristics, allowing the system to select or combine methods based on availability and required precision, rather than relying on a single monolithic location service.
3Ease of operation
If traditional geofencing with fixed boundaries is used, then ease of operation is improved, but adaptability deteriorates when dealing with unstable location data
Solution Approach 1:
The patent transforms static geofence boundaries into dynamic boundaries that adapt to the estimated error margin of the location determination method being used. When cellular network location services are used with larger error margins, the system automatically enlarges the geofence boundary to account for this uncertainty, preventing false positives and negatives while maintaining the simplicity of geofence-based tracking.
Data Source
AI summary
A system and method for determination the relative location of a mobile object is described that includes building a database of known/expected locations with the exact longitude and latitude for each location. Next, an estimated location for a mobile object is generated using information from the cellular network and an area boundary is defined around the mobile object that defines, with some probability, where the object is actually located. The known locations in the database that fall within the area boundary are then identified and a relative probability is calculated for each known location that indicates its relative likelihood of where the mobile object is actually located. From this information at least the most probable location of the mobile object is determined along with a measure of estimation confidence.