RF Tag Operating System for Low-Bandwidth IoT Data Exchange
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Solution Overview
Problem
Existing asset management systems face challenges such as insufficient network bandwidth, complex data storage and processing requirements, unreliable network connectivity, difficulty in sharing and securing asset information, and the need for centralized databases that are vulnerable to cyber threats, leading to inefficiencies and security risks.
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 secure, distributed data storage and processing, allowing real-time data transfer to cloud platforms while maintaining confidentiality and authenticity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If centralized database is used for asset information storage, then data management capability is improved, but network bandwidth consumption increases and security vulnerabilities increase
Solution Approach 1:
The patent segments the centralized database architecture into distributed edge computing nodes deployed directly on assets. Each edge node stores and processes data locally, eliminating the need to continuously transmit all data to a central server. This segmentation reduces network bandwidth consumption while maintaining data management capabilities through distributed storage and processing.
Solution Approach 2:
The patent introduces an intermediary layer between assets and the centralized database - the edge computing platform. This intermediary performs data filtering, aggregation, and pre-processing locally, transmitting only essential processed data to the central system. This reduces network traffic while preserving centralized data management capabilities for critical information.
2Adaptability or versatility
If centralized database is used for asset information storage, then data management capability is improved, but system security worsens due to cyber attack vulnerabilities
Solution Approach 1:
The patent segments the centralized database into distributed edge nodes across multiple assets. This segmentation means that if one node is compromised, the attack is isolated to that specific asset rather than providing access to the entire database. Each edge node maintains local data security while collectively providing robust data management capabilities.
Solution Approach 2:
The patent enables edge computing nodes to autonomously manage their own data security and processing without requiring constant centralized control. Each node independently filters, processes, and secures its local data, reducing the attack surface exposed to centralized systems while maintaining overall data management coordination through standardized protocols.
3Productivity
If edge intelligence is implemented on assets, then local data processing capability is improved, but data storage capacity requirement increases
Solution Approach 1:
The patent extracts only the essential data processing and intelligence functions needed at the edge, rather than implementing complete local data centers on each asset. Critical processing capabilities are deployed to edge nodes, while bulk storage remains centralized, reducing the storage capacity requirement on individual assets while maintaining local processing productivity.
Solution Approach 2:
The patent transitions from a single-dimension centralized storage model to a multi-dimensional architecture where compute and storage are separated across different layers. Edge nodes provide local processing dimension, while centralized cloud provides bulk storage dimension, allowing assets to have high processing capability without proportionally increasing local storage requirements.
4Adaptability or versatility
If more sensors and intelligence features are added to assets, then asset intelligence capability is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal edge computing platform that can accommodate multiple sensors and intelligence features through standardized interfaces and modular components. This multi-functional platform reduces device complexity by providing a common architecture that supports diverse sensor types and processing requirements, rather than requiring separate custom systems for each function.
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.