Location Sampling Rate Adjustment for Mobile Power Management
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Solution Overview
Problem
Wireless devices experience power drain due to continuous location sampling for mobile location-based services, requiring users to manually turn off services or shut down applications, which defeats the purpose of providing location-based services.
Innovation Solution
A power management system that adjusts location sampling rates through scheduling, including manual and automatic settings, learning modes, and dynamic frequency and duration modifications based on user behavior and location changes, to minimize power consumption while maintaining service availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous location sampling is performed to provide location-based services, then service availability is improved, but power consumption increases
Solution Approach 1:
The patent implements periodic location sampling instead of continuous sampling by establishing schedules that define specific time windows and intervals for location updates. The system transitions from constant monitoring to scheduled periodic monitoring, reducing power consumption while maintaining service availability during designated sampling periods.
Solution Approach 2:
The system dynamically adjusts sampling rates based on learned user behavior patterns and contextual information. Sampling frequency increases during periods when users are likely to move or need location services, and decreases during periods of inactivity, optimizing the balance between service availability and power consumption.
2Use of energy by moving object
If manual scheduling is implemented to reduce sampling frequency, then power consumption decreases, but user convenience deteriorates
Solution Approach 1:
The system automatically learns user behavior patterns and generates optimized sampling schedules without requiring manual user intervention. The learning module analyzes user movement patterns, app usage, and contextual data to autonomously determine optimal sampling times and frequencies, eliminating the need for users to manually configure schedules while achieving power savings.
Solution Approach 2:
The system continuously monitors user behavior and location data, using this feedback to refine and adjust sampling schedules automatically. The learning module processes ongoing user interactions and environmental context to optimize sampling parameters dynamically, ensuring both power efficiency and service availability without user involvement.
3Use of energy by moving object
If automatic learning mode is enabled to optimize sampling schedules, then power management improves, but device complexity increases
Solution Approach 1:
The learning module leverages existing device components and data sources (GPS, accelerometer, calendar, contacts) to perform behavior analysis without requiring dedicated hardware. By reusing existing sensors and data infrastructure for multiple purposes (location tracking, movement detection, schedule optimization), the system achieves intelligent power management while minimizing additional complexity.
Solution Approach 2:
The patent introduces a learning module as an intermediary layer that processes raw user behavior data and translates it into optimized sampling schedules. This intermediary component abstracts the complexity of behavior analysis from the core location sampling functionality, allowing the system to achieve intelligent power management while keeping the overall architecture manageable through modular design.
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AI summary
A power manager for mobile applications using location based services. Generally described, the system and method manages power of a wireless device by adjusting the sampling rate of mobile applications that use location based services. In one illustrative embodiment, the power can be managed by establishing a schedule. The schedule can include settings such as a start time, end time, interval, and duration. These settings can be used to establish a window of time whereby multiple location samplings can take place. The schedule can minimize the user's involvement on a continual basis by allowing manual and automatic scheduling. The power manager can include, but is not limited to, three features to provide better power management for the wireless device. These can include scheduling for location sampling, automatic and learned scheduling, and frequency and duration dynamic modification. Through the power manager, the power supply on the wireless device can be maximized.