Mobile Device Location Prediction via Motion-Triggered Sampling
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Solution Overview
Problem
Continuous location sampling on mobile devices requires an active microprocessor state, preventing deep sleep and reducing battery life, and frequent network connections further drain power, especially in scenarios like location sharing and geo-fencing.
Innovation Solution
An algorithmic-based solution that predicts the location of a user using historical data and metadata, allowing the device to conserve power by reducing network access and entering a power-saving mode, estimating the location based on previous updates and additional data such as speed and vector information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous location sampling is performed using GPS or network-based methods, then location accuracy and availability are improved, but power consumption increases and battery life decreases
Solution Approach 1:
The patent implements periodic location sampling instead of continuous sampling. The system determines whether to sample location based on whether the device is in motion or stationary, and only activates location sampling when movement is detected. This periodic approach significantly reduces power consumption while maintaining location accuracy when needed.
Solution Approach 2:
The system uses the device's own motion detection capabilities (accelerometer, gyroscope) to determine when location sampling should occur. The device self-regulates its location sampling behavior based on its own state, eliminating the need for continuous external triggering and reducing overall power consumption.
2Speed
If the microprocessor remains active for continuous location sampling, then location updates are available in real-time, but the device cannot enter deep sleep state and power saving options are blocked
Solution Approach 1:
The system transitions the microprocessor between active and deep sleep states based on periodic motion detection cycles. During deep sleep, the processor consumes minimal power, and location sampling is reactivated only when motion is detected, enabling both real-time responsiveness and power conservation.
Solution Approach 2:
The system performs preliminary motion detection using low-power sensors before activating the high-power microprocessor for location sampling. This preliminary check ensures the processor only wakes up when necessary, maximizing battery life while maintaining location update availability.
3Reliability
If frequent network connections are established for location transmission to web services, then location sharing and geo-fencing functionality are improved, but additional power drain occurs on the mobile device
Solution Approach 1:
The system establishes network connections periodically based on motion detection rather than continuously. Location data is transmitted to web services only when the device is in motion, reducing the frequency of network connections while maintaining reliable location sharing functionality when the user is actively moving.
4Loss of information
If location updates are accessed frequently from network storage service, then real-time location information is available, but network access requirements prevent power conservation mode
Solution Approach 1:
The system accesses location information from network storage service periodically based on motion detection events rather than continuously. This approach maintains location information availability when needed while minimizing network access frequency to enable power conservation mode during stationary periods.
Data Source
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AI summary
Architecture that facilitates power conservation in mobile devices such as cell phones using prediction. The architecture is an algorithmic-based solution that transforms infrequently-captured geolocation data of an entity into a continuous probable location approximation. Given the location history and additional data about the recent location of the mobile device, the current location of the device can be estimated with some probability. Additionally, given the location history and additional data about the recent location of the device, the probability of the device actually being at a given point on a map is computed.