LLN Urgent Message Detection via ML Correlation
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Solution Overview
Problem
Low-power and lossy networks (LLNs) face challenges in timely delivery of urgent messages, such as power outage and restoration notifications, due to slow network formation processes and high interference, leading to message loss and latency, which is critical for efficient utility operations and customer requirements.
Innovation Solution
The implementation of correlation analysis and machine learning models by a field network director to detect and deduce the power state of neighboring nodes, even if they do not send explicit messages, using metrics like location, link quality, and energy levels, selecting appropriate models like Linear Regression or Naïve Bayesian Classifier based on correlation analysis to infer power outages or restorations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If the normal network formation process (network discovery, authentication, network configuration, and routing configuration) is used, then network stability is improved, but the message delivery speed deteriorates
Solution Approach 1:
The patent pre-establishes message forwarding relationships and routing configurations during normal network operation before power outages occur. When power restoration happens, these pre-configured paths enable immediate message forwarding without waiting for the complete network formation process, thus achieving fast message delivery while maintaining network stability through the pre-validated routing structure.
2Area of stationary object
If hundreds of devices power on simultaneously after power restoration, then network coverage is improved, but interference and collisions increase
Solution Approach 1:
The system pre-establishes message forwarding relationships and identifies potential message sources before power restoration occurs. When devices power on simultaneously, the pre-configured forwarding paths and identified message sources enable coordinated message transmission, reducing random collisions and interference while maintaining comprehensive network coverage across all restored devices.
3Duration of action of stationary object
If super capacitators are provided to each device to prolong node life, then node durability is improved, but message loss increases due to simultaneous power outage
Solution Approach 1:
The patent introduces intermediate forwarding nodes (routers and other devices) that act as mediators to receive, buffer, and forward PON messages from devices with super capacitators. When power outages occur simultaneously across many nodes, these intermediaries ensure message delivery by receiving messages from multiple sources and forwarding them to the network controller, preventing message loss even when individual node capacitors deplete.
Data Source
AI summary
Techniques and mechanisms for detecting and deducing of urgent messages in low-power and lossy networks (LLNs) using a correlation analysis of the nodes within a network and machine learning (ML) models. Utilizing these techniques, a field network director (FND) of the network can determine neighboring devices within the network. ML models may be utilized to determine that based upon receipt of a power outage notification (PON) message and/or a power restoration notification (PRN) message from nodes, neighboring nodes of the nodes may also have suffered a power outage and/or a subsequent power restoration, even if the FND did not receive a corresponding PON message and/or a corresponding PRN message from the neighboring nodes of the network. Thus, loss of power and subsequent power restoration may be handled for large numbers of neighboring nodes within the network, even when only a few PON messages and/or subsequent PRN messages are received.