Network Path Selection Using AI Emission Prediction
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Solution Overview
Problem
Current network routing technologies do not effectively consider emissions and energy sources when selecting network paths, leading to increased carbon footprint and energy consumption, especially as internet usage grows and more devices rely on network connectivity.
Innovation Solution
Implementing a machine learning model at network nodes to analyze data such as weather, renewable energy availability, traffic patterns, and emission data to select a network path that minimizes emissions and energy usage, using AI circuitry to predict emissions and train network nodes based on predicted emissions and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional network routing is used to select network paths, then network connectivity and data transmission are achieved, but emissions and energy consumption increase
Solution Approach 1:
The system performs preliminary actions by collecting emission data, weather information, and renewable energy availability data before routing decisions are made. Machine learning models are trained in advance on historical data to predict emissions for different network paths, enabling informed routing decisions that minimize emissions while maintaining network functionality.
Solution Approach 2:
An intermediary routing system is introduced between the source and destination network nodes. This intermediary system uses machine learning models to analyze multiple factors including emission data, weather conditions, and renewable energy availability, then selects optimal paths that reduce emissions. The intermediary adds complexity but enables the emission-minimizing functionality.
2Measurement precision
If machine learning models are implemented at network nodes to predict emissions, then emission prediction accuracy improves, but computational resources and energy consumption increase
Solution Approach 1:
The system implements partial action by deploying machine learning models selectively at strategic network nodes rather than all nodes. The models process only the specific data relevant to emission prediction (weather data, renewable energy availability, traffic patterns) rather than all possible network data. This partial implementation achieves sufficient prediction accuracy while limiting the energy overhead of running ML models across the entire network.
3Object-affected harmful factors
If network paths are selected to minimize emissions by utilizing renewable energy sources, then carbon footprint is reduced, but network latency may increase
Solution Approach 1:
The routing system dynamically adjusts path selection based on real-time conditions including renewable energy availability, weather patterns, and network traffic. Rather than static routing, the system continuously adapts to changing conditions, selecting paths that minimize emissions when renewable energy is available and switching to lower-latency paths when emission constraints cannot be met, thus dynamically balancing environmental and performance requirements.
Data Source
AI summary
Systems, apparatus, articles of manufacture, and methods are disclosed to reduce emissions in guided network environments. An apparatus includes interface circuitry, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to collect data from respective network nodes corresponding to a request to access information, predict an emission of accessing the information via the respective network nodes using the data, and select a network path including at least one of the network nodes based on the predicted emission.