Connected Vehicle Network Selection via Dynamic Cost Optimization
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Solution Overview
Problem
Connected vehicles face challenges in optimizing data transfer across various wireless networks due to fluctuating bandwidth and pricing, leading to increased costs and inefficient data management.
Innovation Solution
A network connection manager that dynamically selects the lowest-cost wireless network based on real-time pricing information and predictive analysis to minimize data transfer costs while adhering to timing requirements, using a combination of differentiated pricing schemes and advanced algorithms like deep learning for optimal network selection and data transfer timing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If connected vehicles use multiple wireless networks for data transfer, then data transfer flexibility and coverage are improved, but network selection complexity and cost optimization difficulty increase
Solution Approach 1:
The system employs autonomous algorithms including deep learning models that automatically analyze network conditions, predict bandwidth availability, and select optimal networks without human intervention. The machine learning model continuously learns from historical data to improve selection accuracy, enabling the system to self-optimize network choices based on real-time conditions and cost parameters.
Solution Approach 2:
The system implements continuous feedback loops where network performance metrics, cost data, and bandwidth measurements are constantly monitored and fed back to the selection algorithm. This feedback mechanism allows the system to adapt to changing network conditions, adjust to actual performance versus predicted performance, and continuously refine network selection decisions to maintain optimality.
2Loss of energy
If connected vehicles dynamically switch between wireless networks based on real-time conditions, then data transfer cost is reduced, but network switching frequency and system resource consumption increase
Solution Approach 1:
The system performs preliminary analysis of available networks using predictive algorithms before actual data transfer occurs. The deep learning model forecasts bandwidth availability and cost implications in advance, allowing the system to pre-select optimal networks and prepare for data transfer without requiring frequent reactive switching during active communication.
Solution Approach 2:
The network selection system dynamically adjusts its behavior based on current conditions, data transfer priorities, and predicted network performance. The system can modify switching thresholds and decision parameters in real-time, balancing the need for cost optimization against the costs of frequent network switching and system resource consumption.
3Loss of energy
If connected vehicles use differentiated pricing schemes for network selection, then data transfer cost optimization is improved, but calculation complexity and decision-making time increase
Solution Approach 1:
The system transforms complex differentiated pricing parameters into simplified decision metrics using machine learning models. The algorithm processes multiple pricing dimensions (time-based pricing, bandwidth-based pricing, network-specific rates) and converts them into a unified cost prediction that can be quickly compared across available networks, dramatically reducing decision-making time while maintaining optimization accuracy.
Solution Approach 2:
The system creates simplified predictive models that replicate complex pricing calculations in advance. By training machine learning models on historical pricing data and network conditions, the system generates approximate cost predictions that are much faster to compute than exact differentiated pricing calculations, enabling real-time network selection without sacrificing cost optimization.
4Measurement precision
If connected vehicles monitor and analyze real-time pricing information from multiple networks, then cost optimization accuracy is improved, but information processing load and computational requirements increase
Solution Approach 1:
The system extracts and focuses only on the most critical pricing parameters and network conditions that significantly impact cost optimization, filtering out less relevant data. The machine learning model identifies key features from the raw pricing information and network status data, processing only these essential parameters to reduce computational load while maintaining high accuracy in cost optimization decisions.
Data Source
AI summary
Generally described, one or more aspects of the present application correspond to techniques for dynamic management of the network used for data transfer between a connected vehicle and a remote computing system. For example, during navigation a connected vehicle may switch between connections to a number of different networks, and the pricing of these networks may be periodically recalculated based on current network load. The disclosed techniques can select from among available networks to dynamically optimize the cost of data transfer, even as a vehicle navigates through different networks and as network pricing is changed.