Dynamic Data Pipe Selection for Vehicle Applications
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Solution Overview
Problem
Modern vehicles face inefficiencies in data connectivity as existing systems cannot dynamically adjust data usage across multiple connectivity pipes (cellular, smartphone WiFi, and WiFi/DSRC) to optimize cost and performance for various applications, leading to suboptimal data transfer.
Innovation Solution
A system and method that determines the default bandwidth demand and delay tolerance for each application, dynamically adjusts these metrics based on usage, and uses a multi-criteria utility optimization engine to select the most efficient data pipe for data transfer, balancing cost, latency, and energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a particular data pipe is assigned to an application statically, then the system complexity is reduced, but the data transfer efficiency and cost-effectiveness deteriorate
Solution Approach 1:
The patent implements dynamic data pipe selection by continuously monitoring application performance metrics (bandwidth usage, latency, packet loss) and reassigning applications to optimal data pipes in real-time. The system transitions from static assignment to dynamic reassignment based on current network conditions and application requirements, resolving the contradiction between system simplicity and transfer efficiency.
Solution Approach 2:
The system changes the parameter of data pipe assignment from fixed to variable, allowing the same application to use different data pipes (cellular, WiFi, DSRC) depending on performance metrics. This parameter change enables the system to maintain low complexity while achieving high efficiency through adaptive selection based on measurable parameters.
2Speed
If high bandwidth data pipes are used for all applications, then the data transfer speed is improved, but the cost and energy consumption increase
Solution Approach 1:
The patent applies local quality by matching specific data pipes to specific applications based on their individual requirements. Instead of using high bandwidth pipes for all applications, the system assigns high bandwidth pipes (WiFi/DSRC) only to applications that require them, while using lower bandwidth pipes (cellular) for applications with modest requirements, thereby optimizing energy consumption while maintaining necessary transfer speeds.
Solution Approach 2:
The system uses partial action by providing more bandwidth than minimum required only when necessary. It monitors application performance and allocates high bandwidth resources partially and selectively rather than continuously, reducing energy consumption while ensuring sufficient transfer speed when needed.
3Productivity
If multiple data pipes are monitored and dynamically selected, then the data transfer optimization is improved, but the system complexity increases
Solution Approach 1:
The patent implements self-service by allowing applications to effectively select their own optimal data pipe through performance monitoring and metric-based decision-making. The system autonomously evaluates multiple data pipes, compares performance metrics, and makes selection decisions without requiring complex external control mechanisms, thus improving optimization while limiting complexity growth.
4Loss of energy
If the system continuously monitors and reassigns data pipes, then the cost-effectiveness is improved, but the processing overhead increases
Solution Approach 1:
The patent applies periodic action by monitoring data pipe performance metrics at regular intervals and reassigning applications only when performance thresholds are exceeded or significant changes are detected. This periodic monitoring approach reduces continuous processing overhead while maintaining cost-effectiveness by triggering reassignment only when necessary based on measured performance changes.
Data Source
AI summary
A method for determining which of a plurality of data connectivity pipes will be used to transmit data for one or more applications operating on a vehicle. The method identifies which of the applications are active at a particular point in time and identifies a number of available configurations based on the number of applications that are active and the number of the data pipes that are available to transmit the data for the application. The method identifies a plurality of performance metrics for each configuration and determines an optimal performance value for each performance metric from all of the configurations. The method then determines an overall utility function for each configuration that is based on its performance metrics and the optimal performance value and selects a data pipe for each application that is active based on a maximum overall utility function from each application's available overall utility functions.