Backhaul Unit Node Device Association via Data Consumption Pattern Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for associating node devices with backhaul units in networks are either manual, resource-intensive, error-prone, or require additional costly devices like GPS modules, leading to inefficiencies and potential disruptions in smart lighting systems and other applications.
Innovation Solution
A method that identifies association by analyzing data consumption patterns between node devices and backhaul units over time, using existing communication protocols to match and align data consumption patterns without human interference or additional devices, enabling automatic and reliable association.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual association method is used where field engineer takes down unique identifications and uploads to cloud, then association can be established, but much manual effort in the field is needed
Solution Approach 1:
The system performs self-service by automatically establishing associations between backhaul units and node devices through data consumption pattern matching, eliminating the need for field engineers to manually collect and upload device identifiers. The backhaul unit and node device autonomously generate and match their consumption patterns, achieving automatic association without human intervention.
Solution Approach 2:
The manual mechanical process of field engineers physically collecting device information is replaced by an automated electronic system that uses data consumption pattern analysis. Instead of manual data collection and uploading, the system electronically monitors and matches data consumption patterns to automatically establish associations.
2Reliability
If GPS location method is used to establish association, then association can be identified, but each node device requires GPS module which increases system cost and GPS drift reduces reliability
Solution Approach 1:
The solution extracts the association identification function from the physical location domain (GPS) to the data consumption domain. Instead of relying on GPS hardware and location coordinates, the system extracts and analyzes data consumption patterns to establish associations, eliminating the need for GPS modules while improving reliability through deterministic pattern matching.
Solution Approach 2:
Instead of using physical GPS location data, the system creates and uses a digital copy of the data consumption behavior pattern. This virtual fingerprint of data consumption serves as a reliable identifier that can be matched between backhaul units and node devices without requiring physical GPS hardware.
3Productivity
If node devices are powered on/off for association establishment, then association can be detected, but normal operation of customer devices is interrupted and commissioning large installations takes very long time
Solution Approach 1:
The system performs preliminary action by continuously monitoring and recording data consumption patterns during normal device operation. Instead of interrupting operation to establish association, the association identification data is collected in advance during regular device functioning, allowing immediate association establishment without operational disruption.
Solution Approach 2:
The association establishment process maintains continuity of useful action by allowing node devices to operate normally throughout the commissioning process. Data consumption patterns are collected continuously during regular device operation, eliminating the need to power devices on/off and ensuring uninterrupted service while accelerating commissioning.
4Reliability
If edge device is added to detect association, then association can be identified, but cost increases and data security concerns arise
Solution Approach 1:
The backhaul unit performs multiple functions: it provides network connectivity to node devices and simultaneously serves as an association detection device by monitoring data consumption patterns. This multi-functionality eliminates the need for separate edge devices, reducing system complexity and cost while maintaining reliable association identification.
Solution Approach 2:
Instead of adding edge devices as intermediaries, the system uses the existing data consumption pattern as a mediator for association identification. The data consumption pattern serves as an intermediary that links backhaul units and node devices without requiring additional hardware, thereby reducing cost and security concerns.
Data Source
AI summary
A method of identifying association of a backhaul unit to a node device connected to the backhaul unit in a network is disclosed. The network comprises a plurality of backhaul units each arranged for connecting to a node device and for providing network connection to the node device. The method comprises the steps of: obtaining a data consumption pattern related to a node device over a time period; obtaining data consumption patterns of the plurality of backhaul units over the same time period; matching the data consumption pattern of the node device to one of the data consumption patterns of the plurality of backhaul units; and identifying association of the node device to a backhaul unit having the matched data consumption pattern.