Location Data Integrity Evaluation via Multi-Source Comparison
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Solution Overview
Problem
Mobile devices face challenges in determining the integrity of location data from multiple sources, which can be inaccurate or tampered with, leading to unreliable location information.
Innovation Solution
A method on an electronic device that compares location data from different systems, such as GPS, cellular networks, and Wi-Fi, to determine the integrity by calculating expected deviations based on user activity and uncertainty metrics, discarding or adjusting data that does not meet thresholds, and storing reliable data for accurate location determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If location data from multiple systems is used to improve location accuracy, then location precision is improved, but data reliability deteriorates due to potential inaccuracies and tampering
Solution Approach 1:
The patent introduces an intermediary integrity evaluation mechanism that mediates between multiple location data sources and the final location determination. This intermediary layer compares location data from different systems (GPS, cellular, Wi-Fi) against expected deviations based on user activity classification, filtering out unreliable data before it affects the final location accuracy.
Solution Approach 2:
The system implements feedback by continuously monitoring location data integrity through comparison with expected deviations. When location data falls outside acceptable thresholds, the system adjusts its behavior by discarding or modifying unreliable data points, creating a closed-loop feedback mechanism that maintains data reliability while preserving precision.
2Reliability
If location data integrity evaluation is performed to improve data reliability, then data reliability is improved, but system complexity increases
Solution Approach 1:
The integrity evaluation system is segmented into distinct functional components: user activity classification module, expected deviation calculation module, data comparison module, and data filtering module. This segmentation allows each component to perform a specific function independently, making the overall complex system more manageable and maintainable while achieving high data reliability.
3Measurement precision
If expected deviation thresholds are calculated based on user activity to improve location accuracy, then location precision is improved, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-calculating expected deviation thresholds based on user activity classification before actual location data comparison. Activity patterns are classified and baseline deviations are established in advance, so that when location data arrives, the comparison can be performed quickly against pre-computed thresholds rather than requiring complex real-time calculations.
Data Source
AI summary
Among other things, we describe a method that includes, on an electronic device, receiving, from a first location system of the electronic device, first data indicative of a first location of the device at a first time, comparing the first data to second data indicative of a second location of the device at a second time, the second data having been received from a second location system of the electronic device, and based on the comparison, determining whether the first data meets a threshold of location data integrity.