Utility Network Monitoring with Scheduled and Triggered Measurements
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Solution Overview
Problem
Conventional network monitoring systems face challenges in efficiently collecting and prioritizing network performance data, leading to overwhelming alarms, subjective user reports, and difficulty in distinguishing between minor and critical faults, while neglecting user experience.
Innovation Solution
A network monitoring system comprising an asynchronous subsystem for scheduled measurements and a synchronous subsystem for triggered measurements, combined with user report data, to efficiently collect and analyze both objective and subjective data from user equipment, allowing for timely fault identification and prioritization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all measurement data from every base-station is monitored and stored, then network performance can be comprehensively tracked, but computational resources and storage requirements become excessively large
Solution Approach 1:
The patent segments the network monitoring into two distinct subsystems: an asynchronous subsystem for scheduled measurements and a synchronous subsystem for triggered measurements. This segmentation allows the system to handle different types of measurement data through specialized processing paths, reducing the overall computational burden while maintaining comprehensive monitoring capability.
Solution Approach 2:
The patent implements partial action by selectively processing measurements based on their urgency and relevance. The asynchronous subsystem handles routine scheduled measurements with reduced priority, while the synchronous subsystem prioritizes triggered measurements that indicate potential faults. This selective processing approach reduces computational resources required while maintaining reliable fault detection.
2Reliability
If hundreds or thousands of alarms are generated across the network simultaneously, then all network issues are captured, but network operations teams cannot sensibly deal with the volume of alarms
Solution Approach 1:
The patent segments alarm generation into two categories: asynchronous alarms from scheduled measurements and synchronous alarms from triggered measurements. This segmentation enables the system to prioritize synchronous alarms (indicating potential faults) over asynchronous alarms (routine status updates), making alarm management tractable for operations teams while maintaining complete fault detection.
Solution Approach 2:
The patent introduces an intermediary layer that processes and prioritizes alarms before presenting them to operations teams. The synchronous subsystem acts as a mediator that identifies and elevates critical alarms requiring immediate attention, while routine alarms are handled through the asynchronous subsystem. This intermediary processing makes the alarm volume manageable while maintaining comprehensive monitoring.
3Reliability
If subjective user reports are monitored closely, then early network issues can be detected, but determining when to act on user reports becomes difficult due to their subjective nature
Solution Approach 1:
The patent merges subjective user reports with objective network measurement data from both asynchronous and synchronous subsystems. By combining multiple data sources, the system can correlate user-perceived issues with actual network performance metrics, improving the precision of fault detection while maintaining early detection capability. User reports trigger synchronous measurements that provide objective verification.
Data Source
AI summary
A network monitoring system is provided for monitoring performance of a utility supply network. The system comprises an asynchronous subsystem configured to receive scheduled measurements of network performance characteristics from a plurality of user equipments (UEs) connected to the network, and to store the scheduled measurements in a database. The system also comprises a synchronous subsystem configured to receive triggered measurements of network performance characteristics from at least one UE connected to the network, and to store the triggered performance measurements in the database.

