Collaborative environmental sensor networks for indoor air quality
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Solution Overview
Problem
Existing air quality monitoring systems lack the ability to accurately detect and differentiate between indoor and outdoor air pollutant sources, leading to ineffective alert notifications and remediation actions.
Innovation Solution
A distributed environmental sensing system that utilizes a network of indoor air quality (IAQ) sensing devices across multiple structures, coupled with a cloud-based server system, to detect pollutants and determine their sources by comparing sensor data across structures and adjusting sensing modes accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single structure uses air quality sensors to detect pollutants, then the structure can receive alert notifications, but the system cannot differentiate whether the pollutant source is indoor or outdoor
Solution Approach 1:
The patent combines multiple air quality sensors from different structures into a unified networked system. By merging the sensing capabilities of multiple structures and analyzing their collective data, the system can differentiate between indoor and outdoor pollutant sources through comparative analysis, resolving the limitation of single-structure sensors without requiring complex individual sensor upgrades.
Solution Approach 2:
The server acts as an intermediary that receives data from multiple sensors, processes the information comparatively, and generates differentiated alerts. The server mediates between the raw sensor data and the final alert notification, enabling source differentiation through centralized analysis without adding complexity to individual sensor devices.
2Measurement precision
If multiple structures are monitored to differentiate pollutant sources, then accurate source identification is achieved, but the system complexity and data processing requirements increase
Solution Approach 1:
The server is designed with multi-functionality, handling data collection, comparative analysis, source differentiation, and alert generation for multiple structures simultaneously. This universal approach allows the system to achieve accurate source identification across the network without requiring each individual component to be overly complex.
Solution Approach 2:
The system implements feedback mechanisms where sensor data from multiple structures continuously informs the server's analysis, which in turn generates alerts that trigger remediation actions. The feedback loop enables the system to adapt and refine its source identification accuracy over time while managing network complexity through iterative optimization.
3Measurement precision
If sensors operate in high-sensitivity mode continuously, then pollutant detection accuracy is maximized, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts sensor operating modes based on detected conditions. Sensors switch between normal and high-sensitivity modes as needed, with the server coordinating these changes based on comparative data analysis. This dynamic approach maximizes detection accuracy when pollutants are present while minimizing energy consumption during normal conditions.
Solution Approach 2:
The system changes the sensitivity parameter of sensors based on environmental conditions and detection needs. By adjusting the sensitivity parameter dynamically rather than maintaining it at maximum continuously, the system achieves high detection accuracy when required while significantly reducing overall energy consumption of the sensor network.
Data Source
AI summary
Techniques for operating an environmental sensing system are described. In an example, a cloud-based server system receives a first indication that a first type of air pollutant is present within a first structure from a first plurality of indoor air quality (IAQ) sensing devices positioned within the first structure. A second structure within a predefined distance to the first structure is then identified by the server system. The server system then determines that the first type of air pollutant is not present within the second structure. The server system then causes a second plurality of IAQ sensing devices positioned within the second structure to change an operating mode from a normal sensitivity mode to a high-sensitivity mode.