Sensor Network Calibration for Environmental Monitoring
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Solution Overview
Problem
Current environmental monitoring systems face challenges in achieving high spatial density and accuracy for greenhouse gas and air pollutant monitoring due to the limitations of low-cost, low-accuracy sensors, which are inadequate for ambient monitoring, and the high costs and maintenance requirements of high-accuracy instruments.
Innovation Solution
A method and system that utilize a network of distributed sensors with in-situ and remote sensors, performing automatic zero-drift noise-reducing and multivariate scaling corrections by deriving calibration information from adjacent nodes, and applying it locally, along with environmental corrections using temperature, humidity, and air pressure data, to enhance the accuracy of low-cost sensors to match high-accuracy measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If low-cost sensors are used for environmental monitoring, then device cost is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent combines multiple low-cost sensors into a networked system where data from multiple nodes are aggregated and processed together. By merging observations from numerous low-cost sensors with data from a few high-accuracy reference sensors, the system achieves improved measurement precision that would be unattainable with individual low-cost sensors alone, while maintaining cost-effectiveness through the use of primarily low-cost hardware.
Solution Approach 2:
The patent introduces high-accuracy reference sensors as intermediaries that mediate between low-cost sensors and the final measurement output. These reference sensors provide ground truth data that is used to train machine learning models, which then correct and calibrate readings from low-cost sensors. This intermediary approach allows low-cost sensors to achieve high measurement precision without directly using expensive high-accuracy sensors at every measurement point.
Solution Approach 3:
The patent changes the parameter of measurement precision through data processing and calibration rather than through hardware specifications. By applying machine learning algorithms, statistical corrections, and environmental parameter adjustments (temperature, humidity, pressure) to the raw sensor data, the system transforms low-precision hardware readings into high-precision environmental measurements, effectively decoupling measurement precision from device cost.
2Measurement precision
If high-accuracy instruments are used for environmental monitoring, then measurement precision is improved, but device cost and maintenance requirements increase
Solution Approach 1:
The patent segments the sensor network into two functional categories: reference nodes equipped with high-accuracy instruments for calibration and monitoring, and distributed sensing nodes using low-cost sensors for broad coverage. This segmentation allows high-accuracy instruments to be used sparingly only where necessary for calibration and quality control, while the majority of the network uses cost-effective low-cost sensors, thereby reducing overall device cost and maintenance requirements while maintaining high measurement precision.
Solution Approach 2:
The patent uses high-accuracy reference sensors to create a model or reference profile of accurate measurements, which is then copied and applied to correct readings from multiple low-cost sensors. Instead of deploying expensive high-accuracy instruments throughout the entire network, the system creates a calibration model from reference sensors and applies this correction pattern across all low-cost sensor nodes, achieving high measurement precision at low device cost.
3Measurement precision
If sensor density is increased for high spatial resolution monitoring, then measurement precision is improved, but device cost increases
Solution Approach 1:
The patent employs numerous inexpensive low-cost sensors that can be deployed at high density across the monitoring area. These low-cost sensors serve as disposable or easily replaceable units that provide broad spatial coverage without significant investment. By accepting that individual low-cost sensors have shorter operational lifetimes and lower durability, the system achieves high spatial resolution monitoring at reduced device cost, trading individual sensor longevity for network-wide spatial coverage.
Data Source
AI summary
A system and method for monitoring environment employ a dense network of sensor nodes. The method includes obtaining environmental information by combining a plurality of observations; wherein the plurality of observations are made with in-situ sensors and remote sensors; wherein the sensors form a high-density network comprising a plurality of distributed sensors; and wherein an individual sensor node, comprising of one or more sensors, can perform automatic zero-drift or other correction by deriving calibration information from other nearby nodes' observational data. The system can monitor gas concentrations over urban areas, industrial, forest, farm, wetland, power plants and other types of surfaces.