IoT Automation Rule Conflict Detection With Integrated Execution Models
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Solution Overview
Problem
Traditional IoT automation systems lack dynamic verification of control logics, leading to inefficiencies in identifying policy violations due to static analysis and inability to handle non-static variables, which affects the robustness and calibration of system behavior.
Innovation Solution
A method and system for identifying policy violations in IoT automation systems by obtaining and generating models representing behavior, relationships, and functions of sub-systems, constructing an integrated model, and using a Model Verifier Component to detect conflicts and optimize rule sequences, allowing for dynamic verification of control logics based on variable values known at execution time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static/monolithic analysis of system models is used to control IoT automation systems, then the system structure is simple and easy to implement, but the ability to identify policy violations and verify control logics dynamically is insufficient
Solution Approach 1:
The patent segments the control logic verification into multiple independent model components (system model, policy model, execution model) that can be analyzed separately and then integrated. This allows dynamic verification without requiring complete re-analysis of the entire system, thus improving reliability while managing complexity.
Solution Approach 2:
The patent transitions from static analysis to dynamic analysis by introducing time-varying variables and executing models that simulate system behavior over time. The execution model dynamically evaluates control logics against actual system states, enabling identification of policy violations that static analysis cannot detect.
2Measurement precision
If static analysis methods are used for policy verification, then the verification process is simple and fast, but the ability to handle non-static variables and dynamic system behavior is limited
Solution Approach 1:
The patent performs preliminary actions by pre-defining the system model, policy model, and execution model structures before actual verification. The execution model pre-establishes the framework for dynamic variable handling, allowing precise policy violation identification during execution without requiring complex real-time computations.
Solution Approach 2:
The patent creates simplified copies of the actual system behavior through executable models that mimic system dynamics. These models replicate essential system behaviors and variable interactions, enabling precise policy verification without analyzing the full complexity of the real system in real-time.
3Reliability
If dynamic verification with multiple models is implemented, then the identification of policy violations is more accurate and comprehensive, but the computational complexity and processing time increase
Solution Approach 1:
The verification process is segmented into distinct model components (system model for behavior, policy model for rules, execution model for verification) that can be processed independently. This segmentation allows the system to focus computational resources on specific verification tasks, improving accuracy while maintaining efficiency through modular processing.
Solution Approach 2:
The execution model serves as an intermediary between the system model and policy model, translating system behavior into a format suitable for policy verification. This intermediary layer simplifies the interaction between complex models, enabling accurate verification without requiring direct complex interactions between all model components.
4Adaptability or versatility
If traditional static control methods are used, then the system is easier to implement and maintain, but the system cannot adapt to changing conditions and optimize controlling behavior dynamically
Solution Approach 1:
The patent introduces dynamic elements by using time-varying variables and executable models that adapt to changing system conditions. The execution model dynamically evaluates control logics against current system states, allowing the system to adapt its behavior based on real-time conditions while maintaining a structured framework that manages complexity.
Solution Approach 2:
The patent enables adaptability through parameter changes in the execution model that reflect changing system conditions. By allowing model parameters to vary dynamically based on system state, the control system can adapt to different operating conditions without requiring complete reconfiguration, balancing adaptability with implementation simplicity.
Data Source
AI summary
Identifying policy violations for controlling behavior of an Internet of Things (IoT) automation system is provided. Traditional systems and methods provide for controlling IoT based automation system based upon a static analysis of a system model and rules. The embodiments of the proposed disclosure provide for controlling behavior of the IoT automation system by identifying one or more policy violations, wherein the one or more policy violations are identified by generating a plurality of models representing behavior, relationships and functions of one or more sub-systems corresponding to the IoT automation system; extracting a set of modelled rules; constructing, using each of the plurality of models and the set of modelled rules, an integrated model; and identifying, from the integrated model, the one or more policy violations via a Model Verifier Component for controlling behavior of the IoT automation system.