Learning Machine Multicast Tree for LLN Routing
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Solution Overview
Problem
Low Power and Lossy Networks (LLNs) face challenges in routing, Quality of Service (QoS), security, network management, and traffic engineering due to their complex nature, which existing technologies have not adequately addressed using classic algorithms, especially with the large number of nodes and changing conditions.
Innovation Solution
A point-to-multipoint communication infrastructure using learning machines that allows for expert-based knowledge feedback, enabling dynamic multicast trees and on-the-fly requests for supervised learning, allowing learning machines to interact with multiple experts for classification and decision-making, especially in scenarios where initial data is insufficient or network events are unexpected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If classic algorithms are used for network management in LLNs, then implementation is straightforward, but the algorithms are inefficient and cannot handle the large number of nodes and changing conditions
Solution Approach 1:
The patent replaces classic mechanical routing algorithms with learning machines that use machine learning models to automatically adapt to network conditions. The learning machine substitutes traditional algorithmic approaches with data-driven decision-making, enabling efficient handling of large-scale LLNs without requiring complex manual configuration or optimization.
Solution Approach 2:
The learning machine dynamically adjusts its internal parameters and decision-making behavior based on observed network conditions and data patterns. This allows the system to adapt to changing network states, node failures, and traffic variations without requiring reconfiguration of the underlying algorithm structure, thereby maintaining efficiency in dynamic environments.
2Adaptability or versatility
If learning machines are deployed in LLNs, then network adaptability improves, but the complexity of implementing and managing learning machines increases
Solution Approach 1:
The patent introduces a centralized learning machine controller that acts as an intermediary between network data sources and decision-making processes. This intermediary consolidates the complexity of managing multiple learning models, handles data aggregation and processing, and provides unified control, thereby reducing the overall system complexity while maintaining high adaptability across the distributed LLN.
3Measurement precision
If expert-based knowledge feedback is implemented, then classification accuracy improves, but the communication overhead and system complexity increase
Solution Approach 1:
The patent implements a feedback mechanism where experts provide knowledge-based corrections and validations to the learning machine's classifications. This feedback loop allows the system to continuously improve accuracy by learning from expert judgments while maintaining a manageable complexity level through structured feedback protocols and selective expert engagement only when needed.
Solution Approach 2:
Instead of continuously consulting experts for all decisions, the system applies expert-based feedback selectively only when the learning machine's confidence is below a threshold or when unusual patterns are detected. This partial application of expert feedback maintains high accuracy for critical cases while minimizing communication overhead and system complexity for routine operations.
Data Source
AI summary
In one embodiment, techniques are shown and described relating to a point-to-multipoint communication infrastructure for expert-based knowledge feed-back using learning machines. A learning machine may communicate an expert discovery request into a network to discover one or more experts, and then receive from the one or more experts, one or more expert discovery responses. Based on the one or more received expert discovery responses, the learning machine may then build a dynamic multicast tree of experts to assist the learning machine in a computer network.