Adaptive Polling for Mobile Trajectory Prediction
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Solution Overview
Problem
Existing wireless positioning technologies face challenges in accurately estimating mobile device locations with minimal power consumption and cost, often resulting in substantial errors due to inadequate polling frequencies and interpolation methods.
Innovation Solution
A communication system that generates mobility profiles by collecting and analyzing spatiotemporal data from mobile devices, using a mobility profiling subsystem and GIS database to estimate future locations and optimize polling frequencies, thereby enhancing accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-accuracy positioning technologies are used, then location estimation accuracy is improved, but power consumption increases and device cost increases
Solution Approach 1:
The system dynamically adjusts the polling frequency parameter based on the mobile device's movement state. When the device is stationary or moving slowly, polling frequency is reduced to save power. When moving quickly or changing direction, polling frequency increases to maintain accuracy. This parameter adaptation resolves the contradiction between accuracy and power consumption.
Solution Approach 2:
The system transitions from static positioning intervals to dynamic adaptive polling frequencies that respond to real-time movement characteristics. The polling frequency is continuously adjusted based on detected movement patterns, making the system flexible and efficient rather than rigid and power-intensive.
2Measurement precision
If high-accuracy positioning technologies are used, then location estimation accuracy is improved, but device cost and infrastructure cost increase
Solution Approach 1:
The system replaces expensive hardware-based high-accuracy positioning technologies with a software-based adaptive polling system that uses standard wireless communication infrastructure. By substituting mechanical/hardware solutions with algorithmic/software solutions, accuracy is improved without increasing device or infrastructure costs.
Solution Approach 2:
Instead of using expensive dedicated positioning hardware, the system creates a virtual positioning solution through software algorithms that process standard wireless signals. This copying approach uses existing infrastructure to achieve positioning accuracy that would otherwise require expensive specialized equipment.
3Use of energy by moving object
If low polling frequency is used, then power consumption is reduced, but location estimation accuracy deteriorates
Solution Approach 1:
The system uses dynamic adaptive polling frequencies instead of fixed low frequencies. The polling interval adjusts in real-time based on movement detection, ensuring accuracy is maintained during active movement while power is conserved during stationary periods.
Solution Approach 2:
The polling frequency parameter is changed from a static low value to a dynamic value that adapts to movement conditions. This parameter transformation allows the system to achieve both low power consumption and high accuracy by matching polling intensity to actual positioning needs.
4Device complexity
If inadequate interpolation methods are used, then computational complexity is reduced, but location estimation accuracy deteriorates
Solution Approach 1:
The system segments the positioning problem into distinct phases: movement detection, trajectory analysis, and location estimation. By dividing the computational task into segments, complex interpolation is replaced with simpler sequential processing that maintains accuracy while reducing overall computational complexity.
Solution Approach 2:
The system performs preliminary movement detection and trajectory analysis before final location estimation. This preliminary action prepares the data in advance, allowing simpler and more accurate interpolation methods to be used in the final estimation phase rather than attempting complex real-time calculations.
Data Source
AI summary
In accordance with one embodiment of the present disclosure, a method for mobility profiling includes collecting a plurality of locations of a mobile electronic device, determining at least one trajectory for the mobile electronic device from the plurality of locations, and storing the at least one trajectory in computer readable media. Each of the plurality of locations have a spatial and temporal component. Using the at least one trajectory, estimating a future location of the mobile electronic device, and communicating data to the mobile electronic device corresponding to the estimated future location such that the content of the communicated data is based on the future location.