Indoor Positioning Data Preprocessing for Server Scalability
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Solution Overview
Problem
The increasing number of mobile devices in indoor positioning systems leads to substantial data processing overhead on servers, increasing costs and decreasing scalability, particularly in cloud-based systems, due to the large volume of location data generated.
Innovation Solution
Implementing a data processing method where mobile devices preprocess localization data locally using configuration information, mapping raw data to a grid index, and sending pre-processed data to local or cloud servers, with configurable grid size defining precision, and optionally sending raw data for further processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the system processes all location data on cloud servers, then data analytics capability is maintained, but server computational load increases substantially leading to higher cost and lower scalability
Solution Approach 1:
The patent divides the data processing task into two segments: client-side preprocessing and server-side analytics. Mobile devices perform preliminary processing (mapping to grid indices, filtering) locally, while servers handle aggregate analytics. This segmentation reduces server computational load while maintaining analytics capability.
Solution Approach 2:
The patent implements preliminary action by having mobile devices preprocess location data before transmission to servers. Devices perform grid mapping and data filtering locally in advance, so that when data reaches the server, it is already prepared and reduced in volume, significantly lowering server processing requirements.
2Adaptability or versatility
If the system increases the number of mobile devices supported, then customer base expands, but data volume increases leading to higher communication overhead and processing costs
Solution Approach 1:
The patent extracts essential processing operations from the server and places them on mobile devices. By taking out grid mapping and data filtering functions to the client side, the system reduces the quantity of data that needs to be transmitted and processed by servers, enabling scalability without proportional increase in server resources.
Solution Approach 2:
The patent changes the parameter of data representation by mapping continuous location coordinates to discrete grid indices. This parameter transformation significantly reduces data volume and complexity, making the system scalable to large numbers of devices while maintaining spatial resolution through the grid structure.
3Measurement precision
If the system processes location data with high precision, then measurement accuracy is maintained, but data processing overhead increases
Solution Approach 1:
The patent applies local quality by differentiating processing requirements: mobile devices perform high-precision grid mapping and filtering locally, while servers perform aggregate analytics with appropriate precision for their purpose. This localized quality approach maintains measurement precision where needed without unnecessarily high processing overhead everywhere.
Data Source
AI summary
A data processing method (600) for indoor positioning system (100) comprising steps of: providing (S601) configuration information to a mobile device (200); detecting (S602) raw localization data by the mobile device (200); pre-processing (S604) the raw location data by using the configuration information, upon determining (S603) by the mobile device (200) to carry out preprocessing locally; sending (S605) pre-processed localization data to a cloud server (300) or a local server (400) on premises.