Dynamic Data Collection Indicator for Reducing Network Bandwidth
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Solution Overview
Problem
Existing data collection methods for vehicles on busy roads result in redundant data uploads, straining network connectivity and processing resources, while methods to reduce data on busy roads also diminish data collection on less busy roads, affecting data reliability and resource allocation.
Innovation Solution
A method and apparatus that adjust data collection indicators based on traffic volume and desired confidence intervals, separating data points into transmission and non-transmission groups, allowing only necessary data to be uploaded, thereby conserving resources without compromising data reliability on less busy roads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If always-on collection methods are used to collect probe data, then data availability is improved, but network bandwidth consumption and processing resources increase due to redundant data on busy streets
Solution Approach 1:
The system dynamically changes the data collection parameter (collection frequency) based on road traffic conditions. Collection indicators are adjusted according to traffic volume metrics, allowing frequent collection on busy roads and less frequent collection on less busy roads, thereby optimizing network bandwidth usage while maintaining data availability
Solution Approach 2:
The data collection system transitions from static always-on collection to dynamic adaptive collection. Collection indicators are repeatedly updated based on current traffic conditions, allowing the system to respond to changing road conditions and optimize resource allocation in real-time
2Loss of energy
If campaign definitions with coarse geographic restrictions are used to reduce data collection on busy roads, then network bandwidth consumption is reduced, but data reliability decreases on less busy roads
Solution Approach 1:
The system applies different collection indicators to different road segments based on their specific traffic characteristics. Each road segment receives a customized collection indicator tailored to its local conditions, ensuring adequate data collection on less busy roads while reducing redundancy on busy roads, rather than applying uniform coarse geographic restrictions
Solution Approach 2:
The system divides the road network into discrete road segments and applies individualized collection indicators to each segment. This segmentation allows precise control over data collection at the segment level, preventing the loss of data reliability on less busy roads that occurs with coarse geographic restrictions
3Reliability
If the number of data points collected is increased to improve prediction reliability, then data reliability is improved, but processing resources and network bandwidth increase
Solution Approach 1:
The system optimizes the parameter of data point quantity by adjusting collection indicators based on traffic volume. This ensures sufficient data points are collected for reliable predictions on all roads while preventing excessive data collection on busy roads, thereby improving processing efficiency without sacrificing prediction reliability
Data Source
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AI summary
A method, apparatus, and computer program product are provided to reduce redundant data uploads, the method includes, for example, providing a collection indicator for a specific data type on a given road. The method further includes separating a plurality of data points for the specific data type collected during travel of a vehicle along the given road into a transmission group of data points and a non-transmission group of data points. The separating of the plurality of data points may be done by determining an amount of data points to be included in the transmission group by applying the collection indicator to the plurality of data points for the specific data type that are collected during travel of the vehicle along the given road. The method still further includes causing the transmission of the transmission group of data points.