Metadata-Driven Computing System for IoT Interoperability
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Solution Overview
Problem
Current technologies lack a simple and scalable way to distribute and continuously update systems across different devices, and there is no effective method for executing applications on various devices from appliances to smartphones, leading to fragmented systems with complex and costly integrations, especially in supporting semantic interoperability for IoT and Edge Computing.
Innovation Solution
A computing system with a digital message format, message broker, and I/O processor facilitates unified management, automation, and interoperability by distributing and synchronizing digital representations of objects, processing events, and generating datasets across devices, enabling real-time event-driven process orchestration and universal semantic interoperability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional software architecture patterns and communication protocols are used for device communication, then device interoperability can be achieved, but semantic interoperability requirements for IoT and Edge Computing cannot be effectively supported, leading to fragmented systems
Solution Approach 1:
The patent creates a universal computing system that can execute on any device regardless of architecture, using a standardized instruction set and runtime environment. This multi-functional approach allows the same system to run on diverse devices (from appliances to smartphones) while maintaining semantic interoperability, eliminating the need for device-specific implementations and reducing system fragmentation.
Solution Approach 2:
The patent introduces an intermediary layer consisting of a runtime environment and instruction set architecture that mediates between the application layer and hardware layer. This intermediary enables semantic interoperability by providing standardized interfaces and data models, allowing different devices to communicate meaningfully without requiring complex device-specific integrations.
2Adaptability or versatility
If virtual machines are used to run applications on different machines, then application portability is improved, but there is still no simple and scalable way to distribute and continuously update systems across devices
Solution Approach 1:
The patent implements a dynamic system distribution mechanism where the computing system can be continuously updated and redistributed across devices in real-time. The runtime environment enables dynamic loading, updating, and execution of system components without requiring full virtual machine deployments, allowing for efficient continuous integration and delivery of updates across the device network.
Solution Approach 2:
The patent segments the computing system into modular components that can be independently distributed and updated. Rather than distributing entire virtual machine images, the system divides functionality into reusable components that can be selectively deployed and updated across devices, improving distribution efficiency and enabling incremental updates.
3Manufacturing precision
If applications are specifically designed and implemented for each device, then device-specific optimization is achieved, but integration becomes complex and costly across disparate systems
Solution Approach 1:
The patent creates a universal computing platform that maintains device-specific optimization capabilities through standardized interfaces. The runtime environment and instruction set architecture enable applications to be optimized for specific device characteristics while maintaining compatibility across the platform, eliminating the need for separate application versions for each device type.
Solution Approach 2:
The patent utilizes parameter changes in the runtime environment to adapt the computing system to different device characteristics. By dynamically adjusting execution parameters, memory management, and resource allocation based on device capabilities, the system achieves device-specific optimization without requiring separate application implementations, thereby reducing integration complexity.
Data Source
AI summary
Unified management, automation and interoperability of business and device processes utilizing components of a metadata-driven computing system on any device and/or across difference devices. In an embodiment, a I/O processor on a device receives an input dataset, wherein the input dataset may be a messages dataset received from a message broker. The I/O processor accesses one or more instructions datasets nested within a state dataset to process each row in the input dataset. Processing of the input dataset by the I/O processor updates the state of the state dataset and may output one or more datasets, wherein an output dataset may be a messages dataset sent to a message broker to send to a computing system for processing. A messages dataset may comprise one or more messages, wherein a message may comprise one or more events, queries, or query results for processing by a computing system.