RF Tag OS With IoT Connector Core for Secure Edge Asset Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing asset management systems face challenges with network bandwidth, data storage, processing speed, security, and connectivity, particularly in machine-to-machine configurations, and require improved methods for organizing, managing, and securing asset information across diverse stakeholders with unreliable network connectivity and complex infrastructure.
Innovation Solution
A tag operating system integrated with an IoT connector core that manages asset data exchange between RF readers and tags, enabling local intelligence and real-time data transfer to cloud platforms, using endpoint intelligence functions and secure data protocols, and supports diverse tag types with a comprehensive software library for seamless integration with IoT systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud-based centralized data storage and processing is used, then data security and centralized management are improved, but network bandwidth consumption increases and processing speed decreases
Solution Approach 1:
The patent segments the asset management system into edge devices (readers and tags) that perform local data processing and a cloud platform that provides centralized management. The edge device executes local intelligence functions to process sensor data and generate asset data locally, reducing the need for continuous cloud communication while maintaining security through selective data synchronization to the cloud platform.
2Adaptability or versatility
If more sensors and intelligence functions are added to assets, then asset monitoring capability is improved, but network bandwidth consumption and data storage requirements increase
Solution Approach 1:
The patent implements preliminary action by pre-configuring the edge device with a software library containing multiple intelligence functions and rules before deployment. The edge device processes sensor data locally using these pre-loaded functions, filtering and processing data at the source to generate only relevant asset data for cloud synchronization, thereby reducing overall data volume despite increased monitoring capabilities.
3Speed
If local intelligence functions are executed on edge devices, then processing speed and responsiveness are improved, but device complexity increases
Solution Approach 1:
The patent applies universality by designing a modular software library that contains multiple intelligence functions (detection, computation, logic operations, rule-based operations) that can be selectively executed. The edge device uses a universal processing architecture that can handle various sensor types and intelligence functions through a common interface, reducing complexity despite the versatility of operations.
4Loss of energy
If distributed data storage on edge devices is implemented, then network bandwidth consumption is reduced, but data organization and retrieval complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where the edge device continuously monitors asset state and environmental conditions, processes this data locally using intelligence functions, and only transmits processed asset data to the cloud platform when changes occur or at scheduled intervals. This feedback-driven approach reduces unnecessary data transmission while maintaining organized data structures through local processing and selective synchronization.
Data Source
AI summary
In embodiments of the present disclosure improved capabilities are described for a tag operating system configured to manage asset data exchanged between an RF reader and one or more asset RF tags, and an IoT connector core integrated with the tag operating system, where the IoT connector core transfers asset data collected via the RF reader from the one or more asset RF tags and delivers the asset data to an IoT system of a cloud platform over a data exchange protocol, and where the asset data was generated at least in part based on an endpoint intelligence function stored on the one or more asset RF tags.