Cloud-Offloaded GPS Positioning with On-Device GPU Batch Processing
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Solution Overview
Problem
Existing Global Navigation Satellite Systems (GNSS) face challenges in efficiently calculating the position of moving objects due to high energy consumption and excessive network bandwidth usage, particularly in applications like wildlife tracking where devices may be disconnected for long periods, leading to large data accumulation and increased battery drain.
Innovation Solution
The Cloud-Offloaded Device-Acquisition Global-Positioning-System (CODA-GPS) method, which utilizes an embedded graphics processing unit (GPU) for parallel processing of GNSS signals, allowing batch processing and reducing the need for continuous real-time processing, thereby minimizing energy consumption and data transfer volume.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous real-time processing of GNSS signals is performed to track position of moving objects, then position tracking accuracy is improved, but energy consumption increases
Solution Approach 1:
The patent implements periodic batch processing of GNSS signals instead of continuous real-time processing. The processor collects raw GNSS signals over a predetermined time period, then performs batch processing to calculate positions at multiple positions. This periodic approach maintains position tracking accuracy while significantly reducing energy consumption by allowing the processor to remain in low-power states between batch processing cycles.
2Measurement precision
If continuous real-time processing of GNSS signals is performed to track position of moving objects, then position tracking accuracy is improved, but network bandwidth usage increases
Solution Approach 1:
The system transmits collected GNSS signal data to the server only during periodic batch processing cycles rather than continuously. This reduces network bandwidth usage significantly while maintaining position tracking accuracy, as the server receives comprehensive data sets for processing rather than continuous streams of data.
Solution Approach 2:
The patent extracts and transmits only the essential raw GNSS signal data to the server for batch processing, rather than transmitting continuous processed position data. This extraction approach reduces network bandwidth consumption by sending only the necessary data for accurate position calculation.
3Loss of information
If data is accumulated during disconnection periods for later transmission, then position data completeness is improved, but battery drain increases
Solution Approach 1:
The system performs preliminary collection of raw GNSS signals during disconnection periods and stores them locally without processing. When connected to the network, the accumulated data is processed in batches. This preliminary action maintains data completeness while minimizing battery drain by deferring energy-intensive processing until network availability, rather than continuously processing or transmitting data.
Data Source
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AI summary
Methods, systems, and computer programs are presented for cloud-offloaded position calculation with on-device acquisition. One method includes operations for collecting raw global positioning system (GPS) signals for a set of positions, and storing in memory the raw GPS signals. The method further includes processing the raw GPS signals in batch mode at the computing device to obtain acquisition data for the positions, where the processing includes identifying tasks for parallel processing by a graphics processing unit (GPU), performing, by the GPU, the tasks in parallel by assigning each of the tasks to a core processor within the GPU, and combining results from performing the tasks to obtain the acquisition data. Additionally, the method includes an operation for transmitting the acquisition data to a server for calculating locations for the set positions at the server, the server storing the locations and making the locations available for location-tracking applications.