Active Learning Framework for Sensor Data Traffic Control
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Solution Overview
Problem
Sensor networks face congestion and latency due to the large volume of data transmitted from numerous sensors to a centralized server, which can overwhelm network resources, especially when using cellular or IP networks.
Innovation Solution
Implementing a machine-based active learning mechanism that generates policies to control data transmission from sensors, determining what data to transmit and how often, thereby reducing the network load by only sending essential data for improving the global model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all sensor data is transmitted to the centralized server, then data completeness is improved, but network congestion and latency increase
Solution Approach 1:
The patent extracts only the essential information from sensor data by using active learning to identify and transmit only the data points that are most useful for improving the global model. This selective extraction reduces network traffic while maintaining the quality and relevance of transmitted data, resolving the contradiction between data completeness and network throughput.
Solution Approach 2:
The system dynamically changes the parameters of data transmission based on model uncertainty and network conditions. The active learning mechanism adjusts which data points to transmit based on their informational value, transforming the static approach of transmitting all data into a dynamic, adaptive transmission strategy that optimizes both completeness and efficiency.
2Measurement precision
If more sensors are added to the network, then measurement coverage is improved, but network load increases
Solution Approach 1:
The system enables sensors to self-regulate their data transmission behavior based on policies generated by the active learning mechanism. Each sensor autonomously determines what data to transmit based on the global model's needs, eliminating the need for centralized control while maintaining optimal data selection. This self-service approach scales well with the number of sensors without proportionally increasing network load.
Solution Approach 2:
The patent applies extraction at the sensor level by having each sensor independently identify and transmit only the data points that contribute most to improving the global model. This localized extraction ensures that measurement coverage is maintained while minimizing the quantity of data transmitted across the network.
3Measurement precision
If data transmission frequency is increased, then model accuracy is improved, but network congestion worsens
Solution Approach 1:
The patent implements dynamic data transmission scheduling where the frequency and timing of data transmissions are adjusted based on real-time network conditions and model needs. The active learning mechanism dynamically determines optimal transmission moments, allowing the system to maintain model accuracy while avoiding unnecessary transmissions that would congest the network.
Solution Approach 2:
The system uses feedback from network conditions and model performance to continuously optimize data transmission strategies. The active learning mechanism receives feedback about which data points are most valuable and adjusts transmission frequency accordingly, creating a closed-loop system that balances model accuracy requirements with network throughput capabilities.
Data Source
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AI summary
Systems and methods for regulating data traffic using active learning techniques. In one embodiment, a global monitor communicates with a plurality of sensors over a wide area network. The global monitor builds a global model for a data analytics service that maps elements to values based on data reported by the sensors. The global monitor generates a query for the data from the sensors, selects one or more candidate elements from the elements in the global model, generates a global policy specifying that the data requested from the sensors is limited to the data targeted to the candidate element(s), and sends the query indicating the global policy to the sensors. The global monitor receives the data targeted to the candidate element(s) from the sensors according to the global policy, and adjusts the global model based on the data targeted to the candidate element(s).