Location Forecasting System for Mobile Energy Reduction
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Solution Overview
Problem
The increasing number of services available on user equipment leads to higher energy consumption, resulting in reduced battery life and inconvenience for consumers, posing a challenge for service providers and device manufacturers to balance service delivery with energy efficiency.
Innovation Solution
A method and apparatus that utilize measured location data to forecast user equipment locations, convert movement data, and compare forecasted locations with actual data to initiate or disable reporting based on deviations, thereby reducing the need for continuous GPS usage and conserving energy by linking multiple services together for seamless operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous GPS usage is implemented for location tracking, then location accuracy is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic GPS measurements rather than continuous tracking, taking location samples at intervals. Between samples, the system uses forecasted location data and sensor-based movement data to estimate current position, thereby reducing GPS activation frequency and energy consumption while maintaining acceptable location accuracy.
Solution Approach 2:
The system introduces forecasted location data and sensor-based movement data as intermediary elements between actual GPS measurements. These intermediaries allow the system to estimate location continuously without continuously activating the energy-intensive GPS receiver, bridging the gap between periodic measurements and continuous tracking needs.
2Adaptability or versatility
If multiple services are provided on user equipment, then service versatility is improved, but energy consumption increases
Solution Approach 1:
The system merges location tracking functionality across multiple services into a single unified mechanism. Instead of each service independently using GPS, the system implements a shared location tracking approach where forecasted location data and sensor data are utilized by multiple services, reducing redundant GPS activations and overall energy consumption.
Solution Approach 2:
The forecasted location data and sensor-based location estimation system serves as a universal solution for multiple services. Rather than each service implementing its own location tracking, the unified system provides location information to multiple services simultaneously, enabling service versatility while minimizing energy consumption through shared resource utilization.
3Measurement precision
If location data is continuously reported, then location monitoring accuracy is improved, but communication energy consumption increases
Solution Approach 1:
The system extracts and reports only significant location deviations rather than continuously reporting all location data. By comparing forecasted location with actual sensor-based location and identifying only meaningful deviations, the system reduces communication frequency and energy consumption while maintaining effective location monitoring accuracy.
Solution Approach 2:
Instead of continuously reporting all location data, the system implements partial reporting by only transmitting location information when deviations exceed a threshold. This selective approach reduces communication overhead and energy consumption while maintaining sufficient monitoring accuracy for the application's needs.
Data Source
AI summary
An approach is provided for energy-efficient location tracking. An energy saving module obtains measured location data of the user equipment, and determines a function to forecast locations of the user equipment based on the measured location data to output forecast location data. The energy saving module further receives movement data from a sensor of the user equipment, and converts the movement data to converted location data. The energy saving module then compares the forecast location data with the converted location data for a deviation, and then it either initiates reporting of the deviation when the deviation exceeds a predetermined range, or disables the reporting of the deviation when the deviation is within the predetermined range.