IoT Digital Twin Simulation for Anomaly Detection
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Solution Overview
Problem
The increasing connectivity of network-enabled components in IoT systems poses challenges in real-time monitoring and identification of compromised, malfunctioning, or underperforming components, as well as the need for immediate corrective measures without disrupting system performance, while also risking data security and integrity.
Innovation Solution
Implementing a method that receives real-time data from real-world components, generates virtual representations, and simulates interactions with lab components in a virtual environment to determine performance characteristics and identify anomalies, allowing for remote corrective actions such as component replacement or upgrades, while maintaining system operational state and ensuring data security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring and simulation of IoT components is implemented, then system reliability and anomaly detection capability are improved, but device complexity and computational resources required increase
Solution Approach 1:
The patent creates virtual copies (digital twins) of physical IoT components that replicate their behavior and characteristics. These virtual components allow for simulation and analysis without affecting the physical system, enabling reliability improvement while managing complexity through virtualization rather than physical duplication
Solution Approach 2:
The patent introduces a simulation environment as an intermediary layer between the physical IoT components and the monitoring/analysis systems. This intermediary virtual environment handles the complex computational tasks of monitoring and anomaly detection, isolating the complexity from the physical system while maintaining system reliability
2Duration of action of stationary object
If immediate corrective actions are taken in the virtual environment, then system uptime is maintained, but the complexity of coordinating virtual and physical systems increases
Solution Approach 1:
The patent performs corrective actions in the virtual environment before implementing them in the physical system. By testing and validating fixes in the virtual copy first, the system can prepare remediation strategies in advance, reducing actual downtime while managing coordination complexity through a controlled rollout process
Solution Approach 2:
The virtual copy serves as a safe testing ground for corrective actions. Changes can be implemented and validated in the virtual environment without risking physical system stability, allowing for quicker rollback or adjustment if issues arise, thus maintaining higher uptime while managing complexity through iterative validation
3Measurement precision
If comprehensive data collection from real and lab components is performed, then measurement precision and anomaly detection accuracy are improved, but data security risks and processing overhead increase
Solution Approach 1:
The patent uses the virtual environment as an intermediary data processing layer. Sensitive data from physical components is processed and analyzed in the controlled virtual environment rather than directly in the physical system or external systems, reducing exposure to security risks while maintaining high measurement precision through comprehensive data analysis
Solution Approach 2:
The patent creates virtual copies of data and components for analysis purposes. Instead of handling sensitive physical system data directly, the system works with replicated virtual data that preserves all analytical value while eliminating security risks associated with accessing and processing actual physical system data
Data Source
AI summary
Systems and methods may include receiving real-time data about a real component operating in a real-world environment. The systems and methods may further include generating a virtual representation of the real component based on the real-time data about the real component and historical data associated with the real component. In addition, the systems and methods may include receiving injected data from a lab. The injected data may provide data about a lab component operating in the lab. The systems and methods may also include simulating, in a virtual environment, a real-time interaction in the real-world environment between the real component and the lab component using the virtual representation of the real component and the injected data. Moreover, the systems and methods may include determining a real-time performance characteristic of at least one of the lab component and the real component based on the simulated real-time interaction in the real-world environment.