AI Expert System for IoT Sensor Data Prioritization
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Solution Overview
Problem
Current monitoring systems for IoT networks fail to fully leverage modern AI, expert system, fuzzy logic, and neural network technologies to provide comprehensive assessments of sensor data and telecommunication network status, leading to inadequate integration and presentation of information for potentially dangerous situations.
Innovation Solution
The development of an AI-powered IoT sensor network with remote sensor stations equipped with electronic AI expert systems, capable of processing sensor inputs, generating control outputs, and prioritizing urgent concerns through hierarchical Multiple-Input/Multiple-Output operations, incorporating fuzzy logic and neural network analysis for integrated and understandable assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data-processing application software is used to manage Big Data from IoT sensors, then data storage and basic processing can be performed, but the system becomes inadequate for comprehensive data analysis, search, sharing, transfer, visualization, querying, updating, information privacy protection, and data source access
Solution Approach 1:
The patent segments the data management system into multiple specialized modules including data capture module, storage module, analysis module, search module, sharing module, transfer module, visualization module, querying module, updating module, information privacy protection module, and data source access module. Each module handles specific data processing tasks independently, allowing the system to manage complex Big Data requirements without overwhelming complexity in a single monolithic system.
Solution Approach 2:
The patent creates a universal data management platform that performs multiple functions through integrated modules. The system provides comprehensive data processing capabilities including capture, storage, analysis, search, sharing, transfer, visualization, querying, updating, privacy protection, and access control within a single unified architecture, eliminating the need for multiple separate systems.
2Reliability
If centralized server systems are used to manage Big Data, then data storage and processing can be consolidated, but the system lacks the benefits of distributed processing and becomes a single point of failure
Solution Approach 1:
The patent divides the data management system into distributed nodes including sensor devices, edge computing devices, and cloud-based servers. Each node operates semi-independently, processing and managing data locally before communicating with other nodes. This segmentation provides redundancy and fault tolerance while maintaining system functionality even if individual nodes fail.
Solution Approach 2:
The patent introduces edge computing devices as intermediary components between sensor devices and centralized cloud servers. These edge devices pre-process data locally, filter information, and manage local storage, reducing the burden on centralized servers while providing a buffer layer that enhances system reliability and reduces latency.
3Measurement precision
If comprehensive monitoring of all sensor inputs is performed, then complete assessment of IoT network status is achieved, but the system becomes overwhelmed by the massive amounts of data requiring processing
Solution Approach 1:
The patent extracts and filters only the most relevant and critical data from the massive sensor inputs at the edge computing level. The system identifies and extracts key parameters and anomalies while discarding redundant information, allowing comprehensive monitoring without processing every single data point in detail, thus maintaining accuracy while improving processing efficiency.
Solution Approach 2:
The patent implements a multi-level monitoring approach where critical parameters are monitored in detail while less critical parameters receive summary-level attention. The system performs exhaustive analysis on high-priority data streams while using sampling and aggregation for lower-priority streams, achieving comprehensive assessment without the computational burden of uniform detailed processing across all data.
4Loss of information
If detailed and comprehensive information is provided about IoT network status, then complete awareness of dangerous situations is achieved, but the information becomes confusing and difficult to understand for operators
Solution Approach 1:
The patent presents information with varying levels of detail tailored to different user needs and contexts. Critical alerts and dangerous situations are highlighted with prominent, simplified visual indicators, while comprehensive technical details are available on-demand for deeper analysis. The interface adapts the quality and depth of information presentation based on the severity and type of event being monitored.
Solution Approach 2:
The patent transforms complex multi-dimensional sensor data into visual representations across different dimensional formats including graphical displays, color-coded status indicators, hierarchical organization levels, and temporal visualizations. This dimensional transformation converts abstract numerical data into intuitive visual information that maintains completeness while improving comprehensibility for operators.
Data Source
AI summary
IoT Big Data information management and control systems and methods for distributed performance monitoring and critical network fault detection comprising a combination of capabilities including: IoT data collection sensor stations receiving sensor input signals and also connected to monitor units providing communication with other monitor units and/or cloud computing resources via IoT telecommunication links, and wherein a first data collection sensor station has expert predesignated other network elements comprising other data collection sensor stations, monitor units, and/or telecommunications equipment for performance and/or fault monitoring based on criticality to said first data collection sensor station operations, thereby extending monitoring and control operations to include distributed interdependent or critical operations being monitored and analyzed throughout the IoT network, and wherein performance and/or fault monitoring signals received by said first data collection sensor station are analyzed with artificial intelligence, hierarchical expert system algorithms for generation of warning and control signals.