Sensor Deployment Classifier for Utility Infrastructure
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Solution Overview
Problem
Utility service providers face challenges in accurately monitoring and maintaining shared infrastructure components due to the need for effective infrastructure design, deployment, and maintenance, exacerbated by infrastructure sharing obligations, which require up-to-date records of component states and arrangements.
Innovation Solution
A computer-implemented method using a classifier trained on supervised data to determine the suitability of infrastructure component locations for deploying network-connected sensors, considering coverage, performance, and communication suitability, with the classifier identifying the most suitable location for sensor deployment based on input vectors including coverage extent, relative performance, and network communication indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network-connected sensors are deployed to monitor infrastructure components, then infrastructure monitoring capability is improved, but deployment complexity and cost increase
Solution Approach 1:
The system performs preliminary actions by training the classifier model in advance with historical sensor data and infrastructure information before actual deployment. This pre-training enables the system to automatically evaluate and select optimal sensor locations without requiring complex manual site surveys or trial-and-error deployments, thus improving monitoring capability while reducing deployment complexity
Solution Approach 2:
The classifier model acts as an intermediary between raw infrastructure data and sensor deployment decisions. It processes multiple input factors (coverage area, infrastructure performance, network connectivity) and translates them into actionable deployment recommendations, simplifying the overall deployment process while ensuring optimal monitoring coverage
2Area of stationary object
If sensors are deployed to cover more infrastructure components, then monitoring coverage is improved, but sensor density and cost increase
Solution Approach 1:
The system applies local quality by evaluating and selecting specific locations with optimal characteristics for sensor deployment. Rather than uniformly distributing sensors, the classifier identifies locations that maximize coverage efficiency based on local infrastructure density, network connectivity, and performance metrics, thereby achieving extensive coverage with fewer sensors
Solution Approach 2:
The system employs partial action by deploying sensors at strategically selected locations that provide maximum coverage impact, rather than instrumenting all possible infrastructure components. The classifier identifies the critical subset of locations where sensor deployment yields the highest monitoring return, avoiding unnecessary sensor installation in areas with low monitoring value
3Measurement precision
If sensor deployment locations are selected based on multiple criteria, then deployment accuracy is improved, but computational complexity increases
Solution Approach 1:
The system segments the deployment decision process into distinct evaluation dimensions: coverage area assessment, infrastructure performance evaluation, and network connectivity verification. The classifier processes these segmented criteria independently through separate input features, then integrates them to produce deployment recommendations, improving accuracy while managing computational complexity through modular processing
Data Source
AI summary
A computer implemented method of deploying a network connected sensor for sensing characteristics of a plurality of infrastructure components in a transmission network for a utility service is disclosed.