Selective Crowdsourcing for Indoor Multi-Level Positioning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing positioning technologies for mobile devices in indoor multi-level structures face challenges such as unreliable wireless signal reception due to attenuation and multipath effects, leading to inefficient data collection and processing, which wastes resources and provides less useful data for positioning.
Innovation Solution
Implementing selective crowdsourcing techniques that detect environmental changes, such as barometric pressure, to determine transitions between levels and batch process wireless signal observations for reliable data transmission, constructing a virtual stack model for accurate positioning assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If continual and indiscriminate data collection is performed for positioning, then database completeness is improved, but resource consumption (bandwidth, memory, battery) increases significantly
Solution Approach 1:
The system performs selective data collection by capturing wireless signal observations only when specific conditions are met (e.g., when the device is stationary, when signal characteristics change significantly, or when positioning accuracy requirements demand it). This partial action approach collects sufficient data for effective positioning without the excessive resource consumption of continual collection
Solution Approach 2:
The system changes the parameter of data collection frequency from constant to variable based on environmental conditions, device state, and positioning requirements. By dynamically adjusting when and how often observations are collected, the system optimizes the balance between database completeness and resource consumption
2Quantity of substance
If continual and indiscriminate data collection is performed for positioning, then database completeness is improved, but bandwidth usage and data transmission costs increase
Solution Approach 1:
The system extracts only the most valuable and relevant wireless signal observations for transmission to the server. By applying selection criteria to identify and extract high-quality data points (e.g., observations from stable environments, diverse location samples, or signals with unique characteristics), the system reduces unnecessary bandwidth consumption while maintaining database effectiveness
Solution Approach 2:
Instead of transmitting all collected data, the system transmits only a partial subset that meets quality thresholds and diversity requirements. This selective transmission approach provides sufficient data for accurate positioning without the excessive bandwidth usage of indiscriminate data upload
3Productivity
If selective crowdsourcing with batch processing is implemented, then resource efficiency is improved, but positioning data accuracy must be maintained
Solution Approach 1:
The system performs preliminary filtering, validation, and organization of wireless signal observations before batch transmission. By pre-processing data to ensure quality criteria are met (e.g., validating signal strength thresholds, checking for anomalies, organizing by location and time), the system maintains positioning accuracy while enabling efficient batch processing
Solution Approach 2:
The system incorporates feedback mechanisms where positioning performance is continuously monitored and used to adjust data collection and processing parameters. When positioning accuracy degrades, the system can increase data collection frequency or adjust selection criteria, ensuring that efficiency gains do not compromise measurement precision
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the efficiency of data collection and processing, reduces resource waste, and provides more accurate and relevant positioning data, improving the user experience and database construction for indoor multi-level structures.
Implementation Method 1
detecting a transition of a location of a mobile device between levels of a structure based, at least in part, on a change in an environmental condition detected via one or more sensors disposed on the mobile device
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Example methods, apparatuses, and/or articles of manufacture are disclosed herein that may be utilized, in whole or in part, to facilitate and/or support one or more operations and/or techniques for selective crowdsourcing for multi-level positioning, such as positioning in a multi-level structure, for example.