Dynamic Sensor Teaming for IoT Reliability
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Solution Overview
Problem
IoT sensors in IoT systems face challenges with accuracy drift and malfunction over time, leading to increased maintenance costs and reduced operational lifespan due to environmental factors, with existing solutions either being costly or inefficient in resource management.
Innovation Solution
Implementing a sensor management system that dynamically assesses sensor health through adaptive sampling frequencies and teaming of sensors in intermediate states to maintain accuracy and extend operational life, utilizing a multi-state operation model to classify sensors as healthy, moderately healthy, weakly healthy, or unhealthy, and adjusting sampling frequencies based on deployment duration and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensors operate continuously in IoT systems, then data collection capability is maintained, but sensor accuracy drift and malfunction occur over time due to environmental factors
Solution Approach 1:
The patent implements dynamic sensor management where sensors transition between active and inactive states based on their health status. The system continuously monitors sensor accuracy and adjusts operational parameters in real-time, creating a dynamic balance between data collection needs and sensor reliability. This resolves the contradiction by making the system adaptive rather than static.
Solution Approach 2:
The system changes operational parameters including sampling frequency, duty cycle, and operational state based on sensor health metrics. When sensors show signs of degradation, the system modifies their operating parameters to extend useful life while maintaining acceptable accuracy levels, thus preserving productivity while managing reliability degradation.
2Measurement precision
If sensors are replaced when accuracy drifts or malfunctions occur, then measurement precision is maintained, but maintenance costs increase and operational lifespan is reduced
Solution Approach 1:
The system performs preliminary health assessments and predictive analytics to identify sensors approaching failure thresholds before they actually malfunction. By taking preliminary actions such as adjusting sampling rates or switching to backup sensors, the system prevents complete failures and extends the operational life of sensors while maintaining measurement precision within acceptable ranges.
Solution Approach 2:
The patent implements continuous feedback loops where sensor performance data is monitored, analyzed, and used to adjust operational parameters. This feedback mechanism enables the system to maintain measurement precision by detecting accuracy drift early and responding with appropriate corrective actions, thereby extending sensor life and reducing maintenance costs.
3Device complexity
If traditional sensor management is used, then device complexity is low, but resource usage efficiency is poor and maintenance costs are high
Solution Approach 1:
The system applies partial monitoring and management actions based on sensor criticality and health status. Not all sensors are monitored at the same level - the system adjusts the intensity of management actions according to individual sensor needs, optimizing resource usage while maintaining necessary oversight. This selective approach improves efficiency without proportionally increasing complexity.
Data Source
AI summary
A system and method for managing sensors including determining health operation states of the sensors correlative with sensor accuracy, classifying the sensors by their respective health operation state, and teaming two sensors each having a health operation state that is intermediate to give a team having a health operation state that is healthy. The sampling frequency of the sensors to determine sensor accuracy may be dynamic.