Location Reporting Prediction for Battery Conservation
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Solution Overview
Problem
Portable devices face significant battery drain due to frequent location determination and reporting, which is necessary for providing location-based services, leading to reduced battery life.
Innovation Solution
A method and system that predict the most likely location or destination of a user device using historical data and schedules generated from heat map information, minimizing the need for frequent location reporting by determining if the device remains within a predetermined area, thereby reducing active processing and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequent location determination and reporting is performed to provide accurate location-based services, then service accuracy and data reliability are improved, but battery power consumption increases significantly
Solution Approach 1:
The system pre-calculates and stores predicted location trajectories and destinations before the user actually travels there. By using historical location data and machine learning algorithms to predict future locations in advance, the system reduces the need for frequent real-time location determinations, thereby conserving battery power while maintaining service accuracy.
Solution Approach 2:
The system continuously monitors actual location data against predicted trajectories and uses this feedback to refine future predictions. When actual location deviates from predicted patterns, the system adjusts its reporting frequency and prediction models, optimizing the balance between data accuracy and power consumption over time.
2Measurement precision
If the device wakes frequently to determine and report location, then location reporting accuracy is maintained, but battery life is reduced
Solution Approach 1:
Instead of continuous or frequent periodic location reporting, the system implements event-driven periodic action where the device wakes only when location changes exceed predetermined thresholds or when predicted destination is approached. This selective periodic wake-up maintains reporting accuracy for significant location changes while minimizing unnecessary wake-ups that consume battery power.
3Speed
If location is reported frequently to remote servers, then service responsiveness is improved, but network power consumption and data usage increase
Solution Approach 1:
The system extracts and processes location pattern recognition and prediction algorithms locally on the user device rather than requiring all location data to be transmitted to remote servers. By performing predictive analytics locally, the system reduces network traffic and associated power consumption while maintaining fast service responsiveness for actual location updates.
Data Source
AI summary
A method and system for reporting a user location are described. Aspects of the invention minimize the need to report a current location of a user device to a remote server by attempting to predict a most likely location or most likely destination of the user device. As long as the user device does not leave a certain area defined in relation to the predetermined location or destination, the user device refrains from reporting to the remote server. The likely location or likely destination may be determined using a schedule generated from heat map information stored on the remote server. The schedule is used to determine where the user is likely to be traveling based upon the user's current location and/or the time of day.