Adaptive Sensor Reporting for Lost Device Retrieval
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Solution Overview
Problem
Existing methods for locating lost or stolen user devices and apprehending thieves are inefficient, as they often rely on binary authentication that can waste resources and are easily thwarted by thieves, and do not provide a nuanced approach to data exfiltration.
Innovation Solution
A user device that determines the likelihood of an authorized user and selectively transmits a subset of sensor data based on this likelihood, balancing resource usage with the need for effective data collection for retrieval and apprehension.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all sensor data is transmitted continuously to assist in locating lost devices, then the utility of data for retrieval and apprehension is improved, but resource consumption (power, memory, bandwidth) increases
Solution Approach 1:
The system dynamically changes the parameter of data transmission by adjusting the subset size of sensor data based on the determined likelihood of unauthorized use. When likelihood is high, more sensor data types are transmitted; when likelihood is low, fewer types are transmitted. This resolves the contradiction by making resource consumption adaptive to the actual security risk level.
Solution Approach 2:
The system transitions from static continuous transmission of all sensor data to dynamic selective transmission. The processor continuously determines the likelihood of unauthorized use and adjusts the subset of sensor data transmitted accordingly. This dynamic approach ensures high data utility when needed while conserving resources during normal operation.
2Ease of operation
If binary authentication is used to trigger data exfiltration, then the simplicity of operation is improved, but accuracy in detecting actual theft is worsened
Solution Approach 1:
The system segments the authentication process into multiple levels: initial binary authentication trigger, followed by continuous likelihood determination based on multiple factors (user behavior, sensor data patterns, time of day, location). This segmentation allows simple triggering while achieving high detection accuracy through progressive refinement.
Solution Approach 2:
The system implements feedback loops where sensor data is continuously analyzed to update the likelihood determination. The processor monitors user interactions, sensor readings, and transmission patterns, adjusting the authentication assessment in real-time. This feedback mechanism maintains operational simplicity while dramatically improving theft detection accuracy.
3Measurement precision
If a nuanced approach with continuous likelihood determination is implemented, then accuracy of theft detection is improved, but device complexity increases
Solution Approach 1:
The processor performs multiple functions using the same hardware resources: it determines authentication status, continuously assesses likelihood of unauthorized use, selects sensor data subsets, and controls transmission. This multi-functionality approach improves detection accuracy without proportionally increasing device complexity, as existing processor capabilities are leveraged efficiently.
Data Source
AI summary
A user device determines a likelihood that a current user of the user device is an authorised user. It then selects a subset of a plurality of types of sensor data obtained by the user device, the subset's size being determined in dependence on the determined likelihood. The user device then transmits one or more reports, each report comprising one or more of the selected one or more types of sensor data.