IoT Profiling and Diagnostics via Anomaly Database Simulation
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Solution Overview
Problem
Traditional IoT diagnosis systems are inadequate in responding dynamically to ongoing threats and predicting potential anomalies, often relying on static rule-based engines that are not robust enough to prevent fatal conditions such as denial of service attacks or equipment failures in real-time.
Innovation Solution
An on-device IoT profiler and smart diagnostics engine that generates an anomaly database through simulation, allowing for real-time matching of observed solution characteristics with anomaly conditions and implementing corrective actions to prevent or reduce the likelihood of failures, using a combination of real and simulated IoT devices for proactive detection and prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static rule-based engines are used for IoT diagnosis, then system simplicity is maintained, but the ability to respond dynamically to ongoing threats and predict potential anomalies deteriorates
Solution Approach 1:
The patent pre-generates an anomaly database through simulation before real-time operation. This preliminary action creates a repository of known anomaly patterns and characteristics that the diagnosis system can quickly match against observed system behavior, enabling dynamic threat response without requiring complex real-time analysis algorithms
Solution Approach 2:
The patent creates a simulated copy of the IoT system to generate anomaly data. By running simulations that replicate various failure modes and attack scenarios, the system builds a comprehensive anomaly database that serves as a reference model for real-time diagnosis, avoiding the need for complex pattern recognition in production systems
2Reliability
If static rule-based engines are used, then device complexity is reduced, but reliability in preventing fatal conditions deteriorates
Solution Approach 1:
The system performs preliminary simulation and anomaly database generation offline before deployment. This pre-computation of anomaly patterns and diagnostic rules ensures comprehensive coverage of potential failure modes without adding runtime complexity to the actual diagnosis engine
Solution Approach 2:
The patent implements a feedback mechanism where observed system characteristics are continuously compared against the pre-generated anomaly database. This feedback loop enables the system to reliably detect and prevent fatal conditions by matching current system state against known anomaly patterns, maintaining simplicity while improving reliability
3Reliability
If real-time anomaly detection is implemented, then system security is improved, but processing time and computational resources increase
Solution Approach 1:
The anomaly database is pre-generated through comprehensive simulation before real-time operation. This preliminary computation stores anomaly patterns, thresholds, and diagnostic rules in advance, allowing the real-time system to perform simple pattern matching rather than complex analysis, thus maintaining high detection accuracy with minimal processing delay
Solution Approach 2:
The patent extracts only the essential anomaly characteristics and patterns from complex simulations into a streamlined database structure. By separating the computationally intensive simulation phase from the lightweight matching phase, the system achieves both high detection accuracy and fast response times
Data Source
AI summary
A computing device and method for profiling and diagnostics in an Internet of Things (IoT) system, including matching an observed solution characteristic of the IoT system to an anomaly in an anomaly database.