Distributed Air Quality Sensor System with Centralized Calibration
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Solution Overview
Problem
Conventional air quality monitoring systems are expensive and require complex calibration procedures, leading to inaccurate data due to outdated calibration information, which cannot be corrected for historic sensor data.
Innovation Solution
A distributed sensor system with spatially distributed base units and a central server that transmits raw sensor data for centralized backend calibration, allowing for the use of updated calibration data to generate accurate calibrated data, and enabling correction of historic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional air quality monitors are used with local calibration, then initial measurement accuracy is achieved, but calibration data becomes outdated over time and cannot be updated for historic data
Solution Approach 1:
A central server acts as an intermediary between sensors and calibration data. The server stores and manages calibration data centrally, receiving raw sensor data from distributed sensors and returning updated calibration data. This intermediary enables calibration data to be updated without modifying the physical sensors, resolving the contradiction between maintaining measurement precision and preventing loss of calibration information.
Solution Approach 2:
The system merges the calibration data storage and management function into a centralized server, combining historical calibration data with current calibration data in one location. This allows all sensors to access updated calibration data from a single source, ensuring measurement precision while preventing outdated calibration data from being lost.
2Measurement precision
If complex calibration procedures are performed locally at each sensor, then initial calibration accuracy is achieved, but the system complexity and cost increase significantly
Solution Approach 1:
The complex calibration procedures and calibration data storage are extracted from individual sensors and relocated to a central server. Sensors only need to transmit raw data and receive calibration factors, while the server handles all complex calibration operations. This extraction reduces device complexity at the sensor level while maintaining calibration accuracy through centralized processing.
Solution Approach 2:
The system enables self-service calibration where sensors automatically receive updated calibration data from the central server without requiring manual intervention or complex local calibration procedures. The server autonomously manages calibration data updates and distributes them to sensors, reducing both device complexity and operational complexity while maintaining measurement precision.
3Reliability
If calibration data is stored locally at each sensor, then initial calibration is maintained, but the data cannot be updated and historic data remains inaccurate
Solution Approach 1:
The system adds a temporal dimension to calibration data management by implementing a version control system at the central server. Instead of static local calibration data, the server maintains historical versions and can retrieve any past calibration data. This dimensional change enables both data integrity (by preserving historical calibration states) and adaptability (by allowing updates and corrections).
Solution Approach 2:
The central server implements a feedback mechanism where it continuously monitors sensor performance and automatically updates calibration data when improvements are detected. This feedback loop maintains data integrity by only applying validated calibration updates while providing adaptability through automatic calibration improvements. The server sends updated calibration data back to sensors, creating a closed-loop system that resolves the contradiction between reliability and adaptability.
Data Source
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AI summary
A distributed sensor system includes a set of spatially distributed base units and a central server both in communication with a data network. Each base unit includes a controller and one or more sensor modules where each sensor module includes a sensor configured to measure an air quality parameter. Each base unit transmits raw sensor data associated with each of the sensor modules over the data network and the central server receives the raw sensor data from the base units and stores the raw sensor data in a database.