Location Authentication Using Environmental Sensor Matching
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Solution Overview
Problem
Existing location-based authentication methods using GPS readings and IP addresses are vulnerable to hacker attacks, as these can be spoofed, leading to unauthorized access.
Innovation Solution
An authentication system that utilizes sensor data such as sound, pollution, temperature, and scent from both user devices and environmental sensors to verify a user's physical location, employing a matching algorithm and machine learning to determine authenticity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If GPS readings or IP addresses are used for location-based authentication, then the authentication process is simple and fast, but the system becomes vulnerable to spoofing attacks and unauthorized access
Solution Approach 1:
The patent introduces environmental sensors (temperature, humidity, light, sound, air quality) as intermediary elements that indirectly verify physical location. These sensors act as mediators between the user and the authentication system, providing environmental fingerprints that are difficult to spoof while maintaining user convenience. The environmental data serves as a trusted third-party verification mechanism.
Solution Approach 2:
The patent replaces the traditional mechanical/location-based verification system (GPS coordinates, IP addresses) with an environmental sensing system. Instead of relying on geometric or network-based location data that can be easily manipulated, the system substitutes environmental parameter measurement, which provides a more secure and tamper-resistant verification method.
2Measurement precision
If multiple sensor parameters are used for environmental matching, then the accuracy of location verification improves, but the device complexity and processing requirements increase
Solution Approach 1:
The patent applies partial action by selecting and using only the most relevant environmental sensor parameters for authentication, rather than requiring all possible sensors. The system can dynamically choose which sensors to activate based on availability and relevance, achieving sufficient accuracy without the full complexity of a complete sensor array.
Solution Approach 2:
The system changes parameters by using multiple different environmental parameters (temperature, humidity, light, sound, air quality) simultaneously. This multi-parameter approach increases verification accuracy through environmental fingerprinting while managing complexity through parameter diversification rather than sensor proliferation.
3Speed
If environmental sensor data is collected and processed in real-time, then the authentication response time is fast, but the energy consumption increases
Solution Approach 1:
The patent implements periodic action by collecting environmental sensor data at specific intervals or triggers rather than continuously. Sensors are activated only when authentication is needed or when environmental conditions change significantly, reducing energy consumption while maintaining fast response times through periodic sampling rather than continuous monitoring.
Solution Approach 2:
The system uses self-service by leveraging the device's existing sensor capabilities and processing power that are already available on modern smartphones and IoT devices. Rather than adding dedicated high-energy authentication hardware, the system utilizes already-present sensors and processors, making the energy cost part of the device's normal operational budget.
Data Source
AI summary
A computer-implemented method includes: (i) receiving location information that represents a physical location of a user; (ii) receiving first sensor data that has been generated by a sensor on a client device of the user; (iii) in response to receiving the first sensor data, obtaining second sensor data that has been generated by a sensor on a sensor device and that represents an environmental condition of an area around the physical location; (iv) determining whether the first sensor data matches the second sensor data; and (v) in response to determining that the first sensor data matches the second sensor data, determining that the user is authentic.