Self-Forming Mesh Control for Secure Medical Digital Twins
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Solution Overview
Problem
Existing digital twin computing systems face challenges in efficiently managing and securing data transmission and control across distributed environments, particularly in real-world premises where objects interact dynamically, leading to inefficiencies in energy management and operational control.
Innovation Solution
A self-forming communication and control system utilizing a mesh network of computing entities that integrate blockchain-encoded records to securely manage data, enabling trusted transfer and control of assets, and AI-driven monitoring to optimize operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a mesh network of computing entities is deployed to enable dynamic data transmission and control, then operational flexibility and real-time monitoring capability are improved, but system complexity and security management difficulty increase
Solution Approach 1:
The system segments the mesh network into multiple zones or regions, each managed by a local coordinator node. This division reduces the overall system complexity by breaking down the large-scale network management into smaller, more manageable units while preserving the operational flexibility of the mesh architecture.
Solution Approach 2:
The patent introduces intermediary components such as gateway nodes and protocol translators that facilitate communication between different parts of the mesh network. These intermediaries simplify security management by providing centralized control points without compromising the distributed nature of the network.
2Reliability
If blockchain-encoded records are used to secure data transmission, then data security and trustworthiness are improved, but processing overhead and energy consumption increase
Solution Approach 1:
The system applies blockchain encoding selectively to only the most critical data transmissions and control commands, rather than encrypting all data packets. This partial application maintains data security for essential operations while significantly reducing the overall energy consumption and processing overhead associated with blockchain operations.
Solution Approach 2:
The patent implements dynamic adjustment of blockchain security parameters such as hash algorithm complexity and block confirmation requirements based on the criticality of the data being transmitted. Less critical data uses lighter cryptographic verification, reducing energy consumption while maintaining adequate security levels.
3Productivity
If AI-driven monitoring is implemented to optimize operations, then operational efficiency and energy management are improved, but computational requirements and system resource demands increase
Solution Approach 1:
The AI monitoring system is segmented into distributed edge AI agents that run locally on mesh network nodes and a centralized cloud-based AI analytics platform. This segmentation allows computational tasks to be distributed, reducing the computational burden on any single device while maintaining high operational efficiency through localized real-time processing.
Solution Approach 2:
The patent employs lightweight machine learning models that are copied and deployed to multiple edge nodes in the mesh network. These simplified model copies enable local intelligence and autonomous decision-making at the edge, reducing the need for constant cloud communication and lowering overall computational requirements while maintaining operational efficiency.
Data Source
AI summary
A method for execution by a computer includes detecting an object of a medical treatment environment based on environment signaling of the medical treatment environment to produce identified medical treatment devices and object profile information. The method further includes facilitating object tracking of the identified medical treatment devices within the medical treatment environment. The method further includes storing object tracking information for the identified medical treatment devices within a digital twin memory for managing the identified medical treatment devices that includes causing a clinical workflow de-assignment.