Mobile Device Measurement Batching for Indoor AP Mapping
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Solution Overview
Problem
Current techniques for wireless access point location mapping are less accurate indoors due to reliance on GNSS signals, which are often unreliable, and consume excessive device resources and bandwidth, especially in crowdsourced data collection systems.
Innovation Solution
Implementing measurement batching on mobile devices to collect and process positioning and access point measurement data, using techniques like pedestrian dead reckoning and Kalman filtering to improve accuracy and reduce resource consumption by sending processed data batches to servers for further analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous data collection is performed to improve AP location mapping accuracy, then measurement precision is improved, but device energy consumption and bandwidth usage increase
Solution Approach 1:
The system implements periodic data collection and processing at the mobile device, where sensors gather positioning and AP measurement data continuously but process and batch this data periodically. This allows the device to maintain accurate measurements while reducing continuous transmission to the server, thereby lowering energy consumption and bandwidth usage while still achieving high AP location mapping accuracy through accumulated batch data.
2Measurement precision
If continuous data collection is performed to improve AP location mapping accuracy, then measurement precision is improved, but network bandwidth consumption increases
Solution Approach 1:
The system implements periodic data collection and processing at the mobile device, where sensors gather positioning and AP measurement data continuously but process and batch this data periodically. This allows the device to maintain accurate measurements while reducing continuous transmission to the server, thereby lowering energy consumption and bandwidth usage while still achieving high AP location mapping accuracy through accumulated batch data.
3Ease of operation
If traditional positioning techniques are used indoors, then device operation is maintained, but measurement precision of AP locations deteriorates due to missing GNSS signals
Solution Approach 1:
The system uses the mobile device as an intermediary carrier that collects both positioning data and AP measurement data locally. Instead of relying on direct GNSS-to-AP mapping, the device accumulates multiple measurements over time and batches them for server processing. This intermediary approach allows the system to maintain device operation with alternative positioning methods indoors while achieving improved AP location accuracy through accumulated batch data processed by the server.
4Measurement precision
If specialized testing engineers are deployed for AP map fingerprinting, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The system enables mobile devices to self-collect and self-process positioning and AP measurement data locally without requiring specialized testing engineers. The mobile device autonomously batches this data and transmits it to the server for processing. This self-service approach achieves high AP location mapping accuracy through accumulated real-world data while dramatically reducing system complexity and deployment cost compared to manual engineering approaches.
Data Source
AI summary
Method, mobile device, computer program product, and apparatus for performing Fine Grain Position Data Collection is described. In one aspect a mobile device performs measurement batching of data, for example positioning and AP measurement data. In some aspects, the mobile device processes positioning and AP measurement data in response to a batch trigger. The mobile device may send the processed measurement batch to a server for analysis or processing. A batch trigger may include receiving a positioning fix with high confidence such that the mobile device may backfilter or otherwise correct mobile sensor based positioning data associated with AP measurements. The server may interpret the data from the measurement batches identified from a number of different mobile devices to calculate position of APs within an environment.