Map-Based Dynamic Location Sampling Using Historical Travel Times
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Solution Overview
Problem
Existing location-based services on mobile devices face significant challenges in minimizing power and resource consumption while maintaining functionality and accuracy, particularly in battery-operated devices, due to high-frequency location sampling by sensors like GPS.
Innovation Solution
A method for dynamic location sampling using time-based extrapolation based on historical traffic data to determine a reduced sampling rate for location sensors, increasing sampling near intersections and reducing it in less congested areas, combined with sanity-check samples to maintain accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed/timed-based intervals are used for location sampling, then location data is continuously captured, but battery resources are consumed excessively
Solution Approach 1:
The patent applies dynamics by transitioning from fixed/timed-based location sampling intervals to dynamic intervals that adjust based on real-time conditions. The system modifies sampling frequency according to device movement state, location uncertainty, and environmental factors, thereby optimizing the balance between location data accuracy and battery consumption.
Solution Approach 2:
The patent implements parameter changes by adjusting the sampling interval parameter dynamically. Instead of using a constant time-based interval, the system varies the sampling interval based on multiple parameters including movement detection results, location uncertainty measurements, and environmental context, achieving energy efficiency while maintaining accuracy.
2Measurement precision
If location sampling frequency is increased to maintain accuracy, then location data quality improves, but power consumption increases
Solution Approach 1:
The patent applies local quality by implementing differential sampling strategies for different spatial and temporal contexts. The system increases sampling frequency only in specific situations (e.g., during movement, near POIs, or when location accuracy is critical) while reducing or suspending sampling during stationary periods or when accuracy requirements are lower, thereby optimizing power consumption relative to actual need.
Solution Approach 2:
The patent implements partial action by sampling at full frequency only when necessary and using reduced or extrapolated sampling otherwise. The system performs location sampling selectively based on detected conditions, using high-frequency sampling partially only during critical moments while relying on lower-frequency sampling or prediction during non-critical periods.
3Reliability
If location data is transmitted frequently, then real-time tracking accuracy improves, but network and battery resources are depleted
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring device state, location quality metrics, and environmental conditions, then using this feedback to dynamically adjust sampling and transmission frequency. The system measures location uncertainty and movement state, feeds this information back into the decision-making process, and adjusts transmission behavior accordingly to maintain reliability while conserving resources.
Solution Approach 2:
The patent applies preliminary action by predicting future location based on current movement state and historical data before actual location sampling occurs. The system performs extrapolation or prediction to estimate upcoming positions, allowing it to reduce actual sampling and transmission frequency while maintaining the appearance of continuous tracking, thereby conserving network and battery resources.
4Measurement precision
If sensors are activated continuously for location tracking, then positioning accuracy is maintained, but device resources are over-consumed
Solution Approach 1:
The patent implements periodic action by using cyclical patterns of sensor activation based on detected conditions. Instead of continuous activation, the system employs periodic sampling intervals that adapt their frequency based on movement detection, location uncertainty thresholds, and contextual factors, thereby maintaining positioning accuracy while significantly improving device resource efficiency.
Data Source
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AI summary
An approach is provided for map-based dynamic location sampling. The approach, for instance, involves calculating an estimated time of arrival at an end node of a road segment from a beginning node of the road segment based on historical traversal time data for the road segment. The approach also involves determining a sampling rate for a location sensor of a vehicle traveling the road segment based on the estimated time of arrival. The approach further involves configuring the location sensor to collect location data using the sampling rate.