Wireless Network Transition Pattern Coding for Cognitive Visibility
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Solution Overview
Problem
Current wireless network technologies lack effective methods to analyze and manage client transitions between access points, leading to inefficiencies and diagnostics challenges due to limited cognitive visibility and inability to infer dependencies or detect anomalies in access point transitions.
Innovation Solution
A network assurance system that represents access points as vertices in a graph and generates client trajectories as subgraphs, allowing for the identification of transition patterns through decomposition, which can be used to effect configuration changes and improve network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional wireless network monitoring methods are used, then the system is simple to operate, but cognitive visibility and ability to analyze client transitions are insufficient
Solution Approach 1:
The patent segments client transition analysis into discrete trajectory units represented as subgraphs. Each client's movement pattern is divided into individual transition segments that can be independently analyzed, stored, and processed. This segmentation enables comprehensive tracking of client behavior without requiring complex centralized monitoring of entire network operations.
Solution Approach 2:
The patent introduces an intermediary data structure (trajectory subgraph) that mediates between raw network data and analytical insights. This intermediary representation captures client transition patterns in a structured format that facilitates pattern recognition and anomaly detection while maintaining system modularity and managing complexity.
2Adaptability or versatility
If no transition pattern analysis is performed, then the system is easier to manufacture and deploy, but inability to detect anomalies and infer dependencies limits network management capability
Solution Approach 1:
The patent performs preliminary action by pre-defining trajectory subgraph structures and transition pattern templates before actual network operation. These pre-configured analytical frameworks enable rapid deployment and immediate pattern recognition capabilities without requiring complex post-installation configuration or training procedures.
Solution Approach 2:
The patent uses copying by creating standardized trajectory subgraph templates that represent common client transition patterns. These templates can be replicated and applied across multiple clients and access points, enabling consistent anomaly detection and pattern analysis throughout the network without requiring custom analysis for each individual case.
3Measurement precision
If detailed client transition tracking is implemented, then user analytics and diagnostics are improved, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent applies local quality by focusing detailed analysis only on specific transition segments and trajectory subgraphs relevant to each client's actual movement patterns. Rather than uniformly processing all possible network data, the system concentrates computational resources on locally relevant transition information, achieving high measurement precision with reduced overall processing complexity.
Solution Approach 2:
The patent segments detailed client transition data into manageable trajectory subgraph units, each representing a specific sequence of access point transitions. This segmentation allows precise tracking of individual client behaviors while enabling parallel processing and distributed analysis, thereby maintaining high measurement precision without overwhelming computational requirements.
Data Source
AI summary
In one embodiment, a device receives data regarding usage of access points in a network by a plurality of clients in the network. The device maintains an access point graph that represents the access points in the network as vertices of the access point graph. The device generates, for each of the plurality of clients, client trajectories as trajectory subgraphs of the access point graph. A particular client trajectory for a particular client comprises a set of edges between a subset of the vertices of the access point graph and represents transitions between access points in the network performed by the particular client. The device identifies a transition pattern from the client trajectories by deconstructing the trajectory subgraphs. The device uses the identified transition pattern to effect a configuration change in the network.