Crowd-sourced Positioning Tile Prioritization
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Solution Overview
Problem
Existing crowd-sourcing based positioning systems face challenges in efficiently prioritizing data collection in important areas due to limited resource consumption and the difficulty in automatically determining the type and context of venues for effective data collection.
Innovation Solution
A method that divides geographical areas into tiles, determines area type and context based on crowd-sourced fingerprint information, and prioritizes data collection in important areas by associating priority information with each tile, using a server or server cloud to manage and process this information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If crowd-sourced data collection is performed globally without prioritization, then positioning data coverage is improved, but resource consumption on mobile devices increases
Solution Approach 1:
The patent applies local quality by differentiating data collection priorities across different geographical areas. Instead of uniform global collection, the system identifies specific high-priority areas (e.g., venues with positioning infrastructure) and concentrates data collection efforts there, while reducing or eliminating collection in low-priority areas. This resolves the contradiction by maintaining reliable positioning coverage in critical locations without requiring continuous global data collection from all mobile devices.
Solution Approach 2:
The patent segments the geographical space into discrete tiles and further into high-priority and low-priority areas based on the presence of positioning infrastructure. This segmentation allows the system to apply different data collection strategies to different segments, concentrating resources on high-priority tiles where positioning data is most needed while reducing burden on devices in low-priority areas.
2Productivity
If data collection is concentrated in high-priority areas, then resource efficiency is improved, but positioning data accuracy in low-priority areas may deteriorate
Solution Approach 1:
The patent changes the parameter of data collection intensity based on the priority classification of geographical areas. High-priority areas receive intensive data collection with higher sampling rates and more frequent updates, while low-priority areas receive reduced or periodic collection. This parameter change optimizes overall system efficiency while maintaining acceptable positioning accuracy in low-priority areas through less frequent but sufficient data gathering.
3Measurement precision
If manual identification of important venues is used, then data collection prioritization accuracy is improved, but system complexity and maintenance cost increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically identify and classify high-priority areas based on detected positioning infrastructure (such as Wi-Fi access points, cellular base stations, or other radio frequency infrastructure). Instead of requiring manual identification and maintenance of venue databases, the system autonomously discovers important areas through crowd-sourced measurements and infrastructure detection, significantly reducing system complexity and maintenance burden while maintaining accurate prioritization.
Data Source
AI summary
A method is provided that includes obtaining one or more pieces of crowd-sourced information. A respective crowd-sourced information is at least indicative of a location at which the respective crowd-sourced information was gathered. The method determines a set of tiles at least partially based on the one or more geographical areas. For at least one tile, the method obtains one or more pieces of fingerprint information comprised by the one or more pieces of crowd-sourced information that were gathered within the respective area of the respective tile and determines an area type and/or context information indicative of a type of venue located within the respective tile and/or context the respective area of the respective tile is used for. The area type and/or context information is determined at least partially based on the one or more pieces of fingerprint information. A corresponding apparatus and computer program product are also provided.