Mobile Device Information Map Construction for Location Accuracy
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Solution Overview
Problem
Mobile devices underutilize their information potential, lacking the ability to effectively gather and utilize contextual location-related information to enhance user experiences, such as determining location, tracking paths, and deducing user behavior within environments like stores, without relying on physical queries.
Innovation Solution
The method involves determining information snippets from various sensors, forming linkages, and constructing an information map using these linkages and a predetermined map to locate the mobile device, incorporating probability values and correlations to refine the device's location based on sensor data and environmental interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mobile devices use multiple sensors and information processing methods to enhance location accuracy, then location determination accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the complex location determination task into multiple independent information sources (sensor snippets from accelerometer, magnetometer, gyroscope, barometer, light sensor, proximity sensor, temperature sensor, humidity sensor, Wi-Fi, Bluetooth, and environmental sensors). Each sensor provides discrete information snippets that are processed separately and then integrated through information linkages to form the complete location solution, reducing the complexity of any single processing component.
Solution Approach 2:
The mobile device utilizes a universal information gathering approach where multiple sensors serve multiple functions. The same sensor data is used for both location determination and user behavior analysis, and the information map structure can represent various types of environmental information beyond just location, making the system multi-functional and reducing overall system complexity through shared components.
2Loss of information
If mobile devices gather and process extensive environmental information, then information content is enhanced, but energy consumption increases
Solution Approach 1:
The system performs preliminary action by pre-processing sensor data into discrete information snippets with defined start and end times as data is collected. This preprocessing organizes the data into manageable units that can be efficiently stored, retrieved, and processed later, reducing the computational energy required during actual location determination and information enhancement operations.
Solution Approach 2:
The system implements partial action by selectively processing information snippets based on their relevance and quality. Not all sensor data is processed equally - the system evaluates information snippets and processes only those that contribute meaningfully to location accuracy, avoiding excessive processing of redundant or low-quality data, thus optimizing energy consumption while maintaining information enhancement.
3Measurement precision
If mobile devices use probabilistic path correlation methods to refine location, then location accuracy is improved, but computational time increases
Solution Approach 1:
The computational process is segmented into distinct phases: information snippet generation, information linkage formation, information map construction, and path correlation. Each phase processes a specific aspect of the data independently, allowing for efficient computation and reducing the overall computational time while maintaining accuracy through systematic processing of probability values and path correlations.
Data Source
AI summary
This disclosure pertains to systems and methods that may be used to increase the information content of a mobile device so as to locate, track, or determine the behavior of a mobile device and/or the user of the mobile device. Separate pieces of information (information snippets) may be aggregated over time to form information linkages. The snippets and linkages may be associated with a time stamp and/or a time frame. The snippets and linkages may be associated with a probability value which may be updated as more information is acquired. The snippets and linkages may be aggregated with, for example a building floor plan to provide more complete informational description of the mobile device and/or the behavior of the user of the mobile device (information map).