Environmental Sensor Data for Location Verification
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Solution Overview
Problem
Traditional location determination systems, such as GPS, can be spoofed, leading to unauthorized access and fraud, as they rely solely on satellite-based navigation and do not independently verify a device's location, which can result in resource-intensive efforts to recover from fraud instances.
Innovation Solution
The system determines a device's location by comparing environmental context data from sensors, such as temperature, air pressure, and ambient light, to a baseline model of the purported location, generating a confidence metric to verify the device's actual location and prevent unauthorized access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS-based location determination is used, then location information can be obtained, but the system is vulnerable to spoofing attacks leading to unauthorized access
Solution Approach 1:
The patent introduces environmental context data as an intermediary verification layer between the GPS location claim and the access authorization. Sensors capture environmental data (temperature, humidity, light, noise) that serves as a mediator to independently verify whether the device is truly at the claimed location, preventing spoofing attacks that only manipulate GPS signals.
Solution Approach 2:
The patent replaces reliance on satellite-based mechanical/navigation systems with environmental sensing-based verification. Instead of depending solely on GPS satellite signals that can be spoofed, the system uses local environmental sensors to detect physical conditions that would be difficult to replicate in a spoofing attack.
2Measurement precision
If environmental sensor data is collected and analyzed, then location verification accuracy is improved, but device complexity and energy consumption increase
Solution Approach 1:
The patent leverages existing multi-functional sensors in mobile devices that serve multiple purposes. Environmental sensors (temperature, humidity, light, noise) are already present for various device functions, and the patent repurposes them for location verification, avoiding the need for dedicated additional hardware and minimizing increased device complexity.
Solution Approach 2:
The system uses the device's own existing sensors and processing capabilities to perform self-verification of location. The mobile device independently captures environmental data and the system analyzes whether the environmental context matches the claimed location, without requiring external verification infrastructure.
3Reliability
If environmental context data is compared with baseline data, then fraud detection capability is enhanced, but data processing requirements and energy consumption increase
Solution Approach 1:
The patent applies partial verification by selecting and comparing only key environmental parameters rather than analyzing all possible sensor data. The system identifies critical environmental factors relevant to specific locations and compares only those, reducing processing requirements while maintaining effective fraud detection capability.
Data Source
AI summary
Techniques are described for determining and/or verifying the location of a device based on environmental data. A device may provide location information describing its location, e.g., determined using a satellite-based navigation system. Environmental context data (e.g., temperature, air pressure, air quality, pollen count, ambient light, etc.) may also be received from the device, having been generated by sensor(s) incorporated into the device. The environmental context data may be compared to corresponding elements of other context data that is generated independently of the device. Based on the comparison, a confidence metric may be determined that indicates a level of confidence that the device's communicated location is its actual location. The confidence metric may be employed to make a security determination regarding a user of the device.