Mobile Device Pre-processing for Real-Time Dead Zone Mapping
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Solution Overview
Problem
Current systems fail to accurately and efficiently map radio frequency transmission dead zones in real time due to inefficiencies in processing large data sets, leading to unreliable and untimely identification, which limits the adoption of offline tracking systems in mobile communication devices.
Innovation Solution
A system and method that preprocesses user data at a mobile communication device before transmission to a cellular-connected server, using algorithms like alpha-shapes to generate a concave hull map, enabling near real-time mapping of dead zones with high accuracy and reducing processing time from hours to seconds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If location data is processed after transmission to the server, then data accuracy is improved, but processing time increases from seconds to hours
Solution Approach 1:
The mobile communication device performs preliminary processing of location data by generating a convex hull of dead zone coordinates before transmission to the server. This pre-processing reduces the data volume and computation burden, enabling the server to quickly aggregate multiple convex hulls and generate the final concave hull map without requiring hours of processing time.
2Measurement precision
If all location data points are transmitted to the server, then mapping accuracy is improved, but network bandwidth consumption and processing load increase
Solution Approach 1:
The system extracts only the essential information needed for dead zone mapping by generating a convex hull from raw location data points at the mobile device. Instead of transmitting all individual GPS coordinates, only the vertices of the convex hull are transmitted to the server, significantly reducing data transmission volume while preserving the boundary information necessary for accurate dead zone mapping.
3Productivity
If complex algorithms are run on the mobile device, then processing speed is improved, but device energy consumption and computational load increase
Solution Approach 1:
The computational workload is segmented between the mobile communication device and the cellular system server. The mobile device performs the computationally intensive convex hull generation locally using its processor, while the server handles the aggregation of multiple convex hulls and final concave hull map generation. This segmentation allows the mobile device to complete processing quickly without requiring excessive energy, as the overall system distributes the computational burden appropriately.
Data Source
AI summary
A system and method are described that include pre-processing of user data at a mobile communication device prior to transmission of the user data to a cellular-connected server. Such pre-processing increases the efficiency of the cellular-connected server to permit near real time mapping of dead zones while maintaining a high degree of accuracy of the dead zone boundary.