Mobile Sensor Routing for Environmental Data Precision
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Solution Overview
Problem
Existing environmental monitoring systems face challenges in efficiently capturing and processing temporal and geographic variations in environmental data, particularly in air quality and greenhouse gas levels, due to limitations in data coverage, reliability, and cost-effectiveness of both stationary and mobile sensor platforms.
Innovation Solution
A method and system for routing mobile sensor platforms to collect environmental data by determining the minimum number of distinct samples and passes over geographic segments, using a reference dataset and precision levels based on relative error rates and false positive rates, to achieve precise and efficient data collection and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mobile sensor platforms collect environmental data continuously and comprehensively, then data coverage and representativeness are improved, but data volume increases leading to processing inefficiency and increased costs
Solution Approach 1:
The system performs preliminary actions by using reference datasets to pre-determine the minimum number of distinct samples and passes required for each geographic segment before actual data collection. This allows the mobile sensor platform to collect only the necessary amount of data needed to meet precision requirements, avoiding unnecessary data collection and processing overhead
Solution Approach 2:
The system changes parameters by dynamically adjusting the number of passes and sampling frequency based on calculated minimum requirements for each geographic segment. Instead of continuous collection, the platform collects data at specific intervals and locations determined by the reference dataset, optimizing the balance between data coverage and processing efficiency
2Measurement precision
If mobile sensor platforms increase the number of passes over geographic segments to improve precision, then measurement precision is improved, but time consumption and operational costs increase
Solution Approach 1:
The system performs preliminary calculations to determine the minimum number of passes required for each geographic segment based on reference datasets and desired precision levels. This allows operators to know in advance exactly how many passes are needed, avoiding unnecessary time consumption from excessive sampling while ensuring precision requirements are met
Solution Approach 2:
The system applies partial action by collecting data from only the minimum necessary number of passes rather than continuous monitoring. The reference dataset enables the system to determine the exact minimum sampling required to achieve desired precision, avoiding excessive data collection that would waste time and resources
3Reliability
If mobile sensor platforms collect detailed hyper-local environmental data, then data representativeness is improved, but data management complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary actions by using reference datasets to pre-calculate the minimum number of distinct samples and passes needed for each geographic segment before deployment. This planning step ensures that hyper-local data collection is targeted and efficient, reducing the complexity of data management by avoiding unnecessary data points while maintaining representativeness
Solution Approach 2:
The system segments the geographic region into discrete segments and determines specific sampling requirements for each segment based on the reference dataset. This segmentation approach allows tailored data collection strategies for different areas, improving data representativeness while managing complexity through structured organization of collection efforts
Data Source
AI summary
A method for routing, through a geographic region over a time interval, a sensor platform mounted on a vehicle is described. The method includes receiving a precision level for at least one constituent of an environment measured by a sensor of the sensor platform. The precision level corresponds to a mean concentration of the constituent(s) over the time interval. A reference dataset corresponding to the geographic region and the time interval is selected. From the reference dataset and the precision level, at least one minimum number of distinct samples for a plurality of geographic segments of the geographic region is determined. The method also includes determining a number of passes for the geographic region over the time interval using the minimum number of distinct samples for each of the plurality of geographic segments. Each pass of the number of passes is part of a route for the vehicle.