Selective Indoor Positioning via Zone-Based Data Filtering
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Solution Overview
Problem
In indoor environments, mobile communication devices face challenges in reliably receiving satellite signals for position estimation, leading to data redundancy and asymmetry in crowdsourced location-related data, which affects the accuracy of localization algorithms and resource utilization.
Innovation Solution
Implementing selective crowdsourcing techniques by using whitelists and blacklists to control the collection of wireless transmitter measurements, allowing data collection in desired areas while inhibiting it in less frequented ones, thereby reducing data redundancy and improving resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If crowdsourced data collection is performed across the entire venue, then the coverage and quantity of location-related data are improved, but data redundancy and asymmetry increase, reducing localization accuracy
Solution Approach 1:
The venue is divided into multiple zones with different data collection policies. High-traffic areas are segmented from low-traffic areas, allowing selective crowdsourcing where data is collected in high-traffic zones and suppressed in low-traffic zones. This segmentation resolves the contradiction by organizing data collection spatially to improve both quantity and precision.
Solution Approach 2:
Different data collection strategies are applied to different spatial regions of the venue. High-traffic areas utilize crowdsourced data collection to maximize data quantity, while low-traffic areas use alternative methods or reduced collection to avoid redundancy. This local differentiation simultaneously achieves adequate data coverage and maintains localization accuracy.
2Area of stationary object
If crowdsourced data collection is performed across the entire venue, then the coverage of location-related data is improved, but resource consumption increases due to data redundancy
Solution Approach 1:
The venue coverage is segmented into high-traffic and low-traffic zones. Data collection is actively performed in high-traffic areas to ensure comprehensive coverage, while collection is suppressed or minimized in low-traffic areas. This segmentation maintains overall coverage while significantly reducing redundant data transmission and processing, thereby conserving power and bandwidth resources.
Solution Approach 2:
Instead of performing full data collection across the entire venue, the system applies partial action by collecting data only in necessary high-traffic regions. This partial collection strategy achieves sufficient coverage for effective localization while avoiding the excessive resource consumption that would result from universal data collection.
3Loss of energy
If selective crowdsourcing is implemented to reduce data redundancy, then resource efficiency is improved, but the complexity of data management increases
Solution Approach 1:
The system performs preliminary action by pre-defining high-traffic and low-traffic zones and establishing data collection policies for each zone before actual crowdsourcing begins. This preliminary configuration simplifies ongoing data management by providing clear, pre-established rules for selective data collection, reducing the complexity of real-time decision-making while maintaining resource efficiency.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor data collection effectiveness and resource consumption. Based on this feedback, the system dynamically adjusts data collection parameters in different zones, optimizing the balance between resource efficiency and management complexity. The feedback loop enables adaptive simplification of data management while preserving resource savings.
Data Source
AI summary
Example methods, apparatuses, or articles of manufacture are disclosed herein that may be utilized, in whole or in part, to facilitate or support one or more operations or techniques for selective crowdsourcing of location-related data, such as within an indoor or like environment, for example, for use in or with a mobile communication device.