Active Data Collection Mode Control for Fingerprint Database Maintenance
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Solution Overview
Problem
Indoor navigation systems face degradation in localization service quality due to discrepancies between fingerprint databases and actual RF signal distributions, leading to high costs and privacy concerns with current data collection methods.
Innovation Solution
A system and method for active data collection mode control that reduces crowd-sourced Wi-Fi/Bluetooth low energy signal data collection by using a mobile device and server to monitor performance changes, selectively activating data collection modes based on change measurement values, and updating the fingerprint database only when significant changes occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous crowd-sourcing data collection is performed to maintain FPDB accuracy, then localization service quality is improved, but energy consumption and privacy risks increase
Solution Approach 1:
The system transitions from continuous data collection to periodic collection triggered by performance degradation detection. The server monitors localization accuracy and only activates crowd-sourcing data collection when performance falls below thresholds, creating an event-driven periodic action pattern that reduces energy consumption while maintaining service quality.
Solution Approach 2:
The system implements self-service through automated performance monitoring and adaptive data collection mode switching. The server automatically detects when FPDB accuracy degrades and triggers appropriate data collection modes without manual intervention, enabling the system to self-regulate resource usage based on actual performance needs.
2Measurement precision
If frequent FPDB updates are performed to reflect site changes, then localization accuracy is maintained, but data collection overhead increases
Solution Approach 1:
The system uses performance-based triggering to determine when FPDB updates are necessary. Instead of frequent periodic updates regardless of need, the server monitors localization accuracy and initiates data collection only when degradation is detected, creating an adaptive periodic update mechanism that reduces unnecessary data collection overhead.
Solution Approach 2:
The server performs preliminary performance evaluation using existing FPDB data before initiating full data collection modes. By monitoring localization accuracy in advance and comparing against thresholds, the system can predict when updates are needed and prepare accordingly, avoiding unnecessary data collection cycles.
3Reliability
If comprehensive signal data is collected to ensure database accuracy, then service quality is improved, but privacy concerns increase
Solution Approach 1:
The system collects only the necessary amount of data required to maintain database accuracy. By using performance monitoring to determine when updates are needed and collecting data only at those moments, the system avoids excessive data collection that would increase privacy risks, while still ensuring database reliability through targeted data gathering.
Solution Approach 2:
The automated performance monitoring and adaptive mode switching system serves as a self-regulating mechanism that minimizes data collection to only what is necessary for maintaining accuracy. This self-service approach inherently limits privacy intrusion by collecting data only when performance degradation is detected, rather than continuously.
Data Source
AI summary
An active data collection mode control system to reduce crowd sourcing signal data collection required for fingerprint database (FPDB) maintenance is provided. The system includes a mobile device that supports a survey mode, a localization mode, and a crowd-sourcing mode, and a server that receives data from the mobile device, generates and updates an FPDB, and controls a data collection mode.