Mesh Network Failure Diagnosis via Triangle Graph Segmentation
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Solution Overview
Problem
Existing mesh network technologies face challenges in accurately identifying and diagnosing failures in pairwise time synchronization and frequency calibration, leading to network-wide errors in reference time and relative locations of transceivers.
Innovation Solution
A method that accesses a network graph representing transceivers and their communication channels, identifies triangle graphs, calculates component diagnostic scores based on edge values, and updates cumulative diagnostic scores for nodes and edges. This method triggers corrective actions when cumulative diagnostic scores exceed thresholds, ensuring transceivers recalibrate or resynchronize to maintain network accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pairwise time synchronization and frequency calibration are performed in a mesh network, then synchronization accuracy is improved, but network complexity increases
Solution Approach 1:
The patent segments the network monitoring function into triangle graphs (groups of three transceivers). Each triangle graph independently calculates diagnostic scores for its constituent transceivers based on pairwise synchronization and calibration data. This segmentation allows the complex network-wide problem to be divided into manageable triangular units, reducing overall system complexity while maintaining accuracy.
Solution Approach 2:
The patent implements a feedback mechanism where cumulative diagnostic scores are continuously updated and compared against thresholds. When a transceiver's score exceeds the threshold, corrective actions are triggered. This closed-loop feedback system automatically maintains synchronization accuracy without requiring manual intervention, managing the complexity through automated responses.
2Measurement precision
If cumulative diagnostic scores are calculated for all transceivers, then failure identification accuracy is improved, but computational load increases
Solution Approach 1:
The computational load is segmented by organizing transceivers into triangle graphs. Each triangle graph calculates diagnostic scores independently using only the pairwise data from its three constituent transceivers. This segmentation prevents the need to process all network data centrally, distributing computational effort and reducing overall load while maintaining accurate failure identification.
Solution Approach 2:
The patent calculates diagnostic scores for all transceivers in the network by processing multiple triangle graphs, which may involve some redundant calculations. However, this excessive action ensures comprehensive coverage and accurate failure identification, as each transceiver is evaluated through multiple triangular relationships, improving reliability despite increased computational effort.
3Reliability
If corrective actions are triggered based on threshold scores, then network reliability is improved, but operational interruptions increase
Solution Approach 1:
The patent triggers corrective actions preemptively when cumulative diagnostic scores exceed thresholds, before actual network failures occur. This preliminary action allows transceivers to be recalibrated or resynchronized in advance, maintaining network reliability and preventing service disruptions rather than reacting after failures manifest.
Solution Approach 2:
The system implements self-service by automatically triggering corrective actions when diagnostic thresholds are exceeded. The mesh network monitors its own health and initiates remediation procedures without external intervention, improving reliability through autonomous operation while minimizing the need for manual operational interruptions.
Data Source
AI summary
A method includes accessing a network graph including: a set of nodes, each representing a transceiver; and a set of edges, each edge connecting two nodes and representing a communication channel between a pair of transceivers. The method also includes: accessing a network state comprising a set of edge values for the set of edges; and identifying a set of triangle graphs in the network graph. The method further includes, for each triangle graph in the network graph: calculating a component diagnostic score based on a subset of edge values; and for each node in the triangle graph, updating a cumulative diagnostic score for the node based on the component diagnostic score. The method additionally includes, in response to detecting a cumulative diagnostic score for a node exceeding a threshold cumulative diagnostic score, triggering a corrective action at a transceiver represented by the node.