IoT Sensor Placement Using Workflow Boundaries and Historic Data
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Solution Overview
Problem
Conventional systems lack the ability to automatically determine optimal types and spatial positioning of IoT sensors in a workspace for effective workflow monitoring, leading to inefficient and often suboptimal sensor placement, which can result in incomplete or inaccurate data collection for key performance indicators (KPIs).
Innovation Solution
A computer-implemented method using machine learning to analyze historic data, determine monitoring boundaries, and recommend the appropriate type and location of IoT sensors within these boundaries for optimal data capture, thereby streamlining the sensor selection and placement process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual trial-and-error methods are used to determine sensor types and positioning, then system complexity is reduced, but measurement precision and productivity deteriorate due to incomplete or inaccurate data collection
Solution Approach 1:
The system performs self-analysis by automatically examining historic workflow data to determine optimal sensor types and positioning, eliminating the need for manual trial-and-error methods. The processor set independently analyzes activity patterns, monitoring boundaries, and information requirements to generate sensor deployment recommendations.
Solution Approach 2:
The system conducts preliminary analysis of historic data before actual sensor deployment, identifying optimal sensor types and locations in advance. This preliminary action includes determining monitoring boundaries and information requirements based on historical workflow patterns, ensuring accurate data collection from the start.
2Productivity
If automated machine learning analysis is implemented to determine optimal sensor positioning, then measurement precision and productivity improve, but device complexity and computational resource requirements increase
Solution Approach 1:
The patent replaces manual mechanical processes of sensor selection and placement with automated computational systems. Machine learning algorithms analyze historic data to determine optimal sensor configurations, substituting human expertise and trial-and-error physical processes with automated digital analysis.
Solution Approach 2:
The system introduces an intermediary processing layer that analyzes historic workflow data and generates sensor deployment recommendations. This intermediary component (processor set with machine learning capabilities) mediates between raw historical data and sensor deployment decisions, automating the complex analysis process.
3Loss of information
If comprehensive historic data analysis is performed using machine learning, then loss of information is reduced, but loss of time increases due to extensive data processing requirements
Solution Approach 1:
The system performs preliminary analysis of historic workflow data in advance, extracting patterns, monitoring boundaries, and information requirements before actual sensor deployment. This preliminary action ensures that when sensors are deployed, they are immediately optimized for complete data collection without requiring extensive real-time analysis.
Data Source
AI summary
A system, method, and computer program product are configured to: analyze historic data of plural activities using machine learning; determine a monitoring boundary for an activity based on the analyzing; determine a type of information for monitoring the activity based on the analyzing; generate a recommendation of a sensor to capture the type of information; and generate a recommendation of a location of the sensor in the monitoring boundary.