Sensor Data Estimation via Grouped Transmission Cycles
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Solution Overview
Problem
The high cost and maintenance burden of deploying and maintaining a large number of environmental sensors, such as those measuring temperature, humidity, and pollutants, due to the frequent need to replace batteries, which is particularly challenging in environments like roads, pipelines, and agricultural land.
Innovation Solution
The sensors are grouped, with each group taking measurements and transmitting data at specific times, allowing for estimated parameter values to be calculated across the entire environment, reducing battery depletion and the frequency of replacements, while also increasing network capacity and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of sensors are distributed throughout the environment to obtain useful environmental data, then measurement coverage and data quality are improved, but deployment cost and maintenance burden increase significantly
Solution Approach 1:
The system divides the environment into multiple zones or regions, each monitored by a subset of sensors. This segmentation allows for more manageable deployment and maintenance while still achieving comprehensive coverage through coordinated data collection across all segments.
2Reliability
If sensors are placed in difficult-to-access locations (e.g., embedded in roads, pipelines) to ensure accurate measurements and prevent dislodgement, then measurement reliability is improved, but maintenance difficulty and cost increase
Solution Approach 1:
The system performs preliminary actions by implementing robust sensor installation protocols during initial deployment, including secure embedding and protective encapsulation. This preliminary securing action ensures long-term stability and reduces the frequency of maintenance interventions needed.
3Productivity
If sensors transmit data frequently to enable real-time monitoring, then data availability and monitoring effectiveness are improved, but battery depletion rate increases, requiring more frequent battery replacements
Solution Approach 1:
Instead of continuous data transmission, the system implements periodic transmission cycles where sensors transmit data at predetermined intervals. This periodic action maintains adequate monitoring capability while significantly reducing power consumption and extending battery operational life.
Solution Approach 2:
The system transmits only essential or threshold-exceeding data rather than all measured data continuously. This partial transmission approach maintains monitoring effectiveness for critical events while reducing overall communication overhead and battery drain.
4Ease of manufacture
If battery-powered sensors are used to reduce installation complexity, then ease of deployment is improved, but maintenance frequency increases due to battery replacement requirements
Solution Approach 1:
The system extends the continuous operational period of sensors by optimizing power consumption through periodic transmission and duty cycling. This continuity extension reduces the frequency of maintenance interruptions and keeps the monitoring system operational for longer periods without intervention.
Data Source
Figure 1
Figure 2A~2B
Figure 3A~3E
AI summary
A data processing method comprising: receiving, from each of a plurality of sensors each located at a respective location, a signal indicative of a respective value of a parameter measured by that sensor at a respective one of a plurality of successive times; and determining, based on the value of the parameter measured by one or more first sensors of the plurality of sensors at a respective one or more of the plurality of successive times, a value of the parameter at the location of a second sensor of the plurality of sensors at one of the one or more of the plurality of successive times.