Cognitive Sensor Fusion Management for Adaptive Data Streams
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Solution Overview
Problem
Current machine learning techniques lack a vertical-agnostic approach for adaptive sensor fusion management, failing to dynamically adjust sensor data streams based on changing conditions and desired outcomes in various business environments.
Innovation Solution
The implementation of cognitive sensor fusion management systems that utilize learning algorithms to automatically discover, onboard, and manage sensor data streams, adjusting the number and type of sensors based on context and desired outcomes through a closed-loop feedback system, optimizing data collection and analysis for specific business verticals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional machine learning techniques are used for sensor data collection, then data analysis can be performed, but the system lacks adaptive management to dynamically adjust sensor data streams based on changing conditions and desired outcomes
Solution Approach 1:
The system dynamically adjusts sensor data stream selection based on changing conditions and desired outcomes. The cognitive sensor fusion management system continuously learns from new data and modifies which sensors are active, what data streams are collected, and how fusion is performed, making the system adaptable rather than static.
Solution Approach 2:
The cognitive sensor fusion management system performs automatic discovery, onboarding, and management of sensor data streams without requiring manual configuration. The system self-adjusts by learning from data patterns and automatically determining optimal sensor combinations for achieving desired outcomes in different business verticals.
2Measurement precision
If more sensors and data streams are used to improve accuracy of desired outcomes, then measurement precision improves, but power consumption and costs increase
Solution Approach 1:
The system uses only the necessary subset of sensors and data streams required to achieve the desired outcome with sufficient accuracy. Rather than continuously using all available sensors, the cognitive system learns to activate only those sensors and data streams that are currently needed, reducing power consumption while maintaining measurement precision.
Solution Approach 2:
The system dynamically changes operational parameters including which sensors are active, data collection frequencies, and fusion algorithms based on current conditions and desired outcomes. This allows the system to optimize the balance between measurement precision and power consumption by adjusting parameters rather than using fixed configurations.
3Adaptability or versatility
If a vertical-agnostic approach is implemented to improve versatility across business environments, then adaptability improves, but the complexity of managing diverse sensor types and workflows increases
Solution Approach 1:
The cognitive sensor fusion management system is designed to be vertical-agnostic, meaning it can operate across different business verticals (manufacturing, energy production, construction, transport, security, enterprises) without requiring vertical-specific customization. The system performs universal functions of automatic sensor discovery, data stream management, and fusion that adapt to any business context.
Solution Approach 2:
The system uses closed-loop feedback to continuously learn from sensor data and outcomes across different verticals. This feedback mechanism allows the system to automatically adapt to diverse workflows and parameters without manual configuration, managing complexity through learning rather than pre-programming for each vertical.
Data Source
AI summary
Systems, methods, and computer-readable for cognitive sensor fusion management include obtaining one or more data streams from one or more sensors. Learning algorithms are used for determining whether a combination of the one or more data streams includes sufficient information for achieving a desired outcome, based on context, business verticals, or other considerations. One or more modifications are determined to at least the one or more data streams or one or more sensors based on whether the combination of the one or more data streams includes sufficient information for achieving the desired outcome. In a closed-loop system, feedback from implementing the one or more modifications can be used to update the desired outcome.