Dynamic Location Sampling Near Wireless Dead Zones for ETA Accuracy
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Solution Overview
Problem
Existing location tracking systems face challenges in accurately determining the estimated time of arrival (ETA) when devices pass through areas with wireless communication loss (dead zones) or experience directional ambiguity, leading to inaccurate path predictions and resource inefficiency.
Innovation Solution
A dynamic location tracking system that adjusts the frequency of location data transmission based on proximity to dead zones or directional ambiguity, using threshold distances to trigger more frequent data sharing when necessary, thereby conserving resources and improving ETA accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If location data is transmitted at frequent intervals, then ETA accuracy is improved, but power consumption and processing resources increase
Solution Approach 1:
The patent applies dynamics by making the location data sampling interval variable rather than fixed. The system dynamically adjusts the sampling interval based on real-time conditions such as proximity to dead zones and directional ambiguity detection. When conditions require higher accuracy (near dead zones or at intersections), the sampling interval decreases (more frequent updates). When conditions are stable, the sampling interval increases (less frequent updates), thus optimizing the balance between ETA accuracy and power consumption.
Solution Approach 2:
The patent changes the parameter of location data sampling interval based on detected conditions. The system monitors factors like distance to dead zones and directional ambiguity, then adjusts the sampling interval parameter accordingly. This parameter change allows the system to allocate more resources (lower interval) when needed for accuracy and fewer resources (higher interval) when accuracy is less critical, resolving the contradiction between measurement precision and energy consumption.
2Use of energy by moving object
If location data sampling interval is increased to conserve resources, then power consumption decreases, but ETA accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by detecting conditions (dead zone proximity, directional ambiguity) before they affect ETA accuracy significantly. The system proactively increases sampling frequency when approaching critical conditions rather than reacting after accuracy has deteriorated. This allows the system to maintain accuracy during critical phases while using lower sampling rates during stable phases, optimizing the overall energy-accuracy tradeoff.
Solution Approach 2:
The system uses feedback from condition monitoring (dead zone detection, directional ambiguity detection) to adjust the sampling interval. The feedback loop continuously assesses whether current sampling frequency is sufficient given current conditions, and adjusts accordingly. This feedback mechanism ensures accuracy is maintained when needed while allowing resource conservation when conditions permit, resolving the contradiction between power consumption and measurement precision.
3Measurement precision
If frequent location data transmission is used, then directional ambiguity is resolved better, but processing resources increase
Solution Approach 1:
The patent applies dynamics by making the data transmission frequency adaptive rather than static. The system dynamically adjusts sampling interval based on directional ambiguity detection and other conditions. When directional ambiguity is detected (e.g., at intersections or near dead zones), the sampling interval decreases to provide more frequent updates for better directional resolution. When the device is in clear directional contexts, the sampling interval increases to reduce processing overhead, thus optimizing the balance between directional accuracy and processing resource consumption.
Solution Approach 2:
The system changes the sampling interval parameter based on detected directional conditions and dead zone proximity. This parameter adjustment allows the system to allocate processing resources dynamically - higher frequency sampling when directional accuracy is critical (near intersections or dead zones) and lower frequency when directional context is stable. This resolves the contradiction by making resource consumption conditional on actual accuracy needs rather than maintaining constant high resource usage.
Data Source
AI summary
Exemplary methods, apparatuses, and systems read location data representing a current location of a mobile device. The location data for the mobile device is taken at a first sampling rate. Upon determining that the location data indicates that the mobile device is approaching a geographic area having a history of wireless communication loss, the mobile device is triggered to increase location data sampling from the first sampling rate to a second sampling rate.