Network Node Commissioning via Optical Correlation
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Solution Overview
Problem
Current light-on-demand systems for municipal lighting face challenges in accurately determining neighbor nodes, leading to inefficient energy use and increased costs due to manual corrections required for maintaining accurate neighbor tables, as the relationship between RSSI values and distances is questionable, resulting in potential misplacement of nodes in the table.
Innovation Solution
A method where a node correlates received indication messages with sensed physical parameters, such as light intensity, to determine neighbor nodes by adding correlation information to its register table when a temporal correlation is established between the parameter change and the message reception, ensuring accurate neighbor node identification without manual adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If RSSI-based neighbor table creation is used, then node identification is automated, but measurement precision deteriorates due to questionable relationship between RSSI values and distances
Solution Approach 1:
The patent changes the measurement parameter from RSSI (signal strength) to optical parameter (light intensity sensed by the sensor). This parameter change enables accurate determination of which nodes are within visual range, thereby achieving precise neighbor identification without manual correction while maintaining full automation.
2Measurement precision
If manual table correction is performed, then node positioning accuracy improves, but ease of operation deteriorates due to required field engineer intervention
Solution Approach 1:
The system performs self-service by automatically detecting which nodes are within visual range through optical sensing and autonomously creating accurate neighbor tables without requiring field engineer intervention. The nodes self-identify their neighbors based on what they can visually detect, eliminating manual correction needs.
3Device complexity
If inaccurate neighbor tables are used, then device complexity is reduced, but energy efficiency deteriorates due to unnecessary node reactions
Solution Approach 1:
The patent replaces the mechanical/algorithmic approach of RSSI-based neighbor detection with an optical sensing mechanism. Nodes use their sensors to detect light intensity and determine which neighbors are within visual range, creating accurate neighbor tables that prevent unnecessary reactions and reduce energy consumption while maintaining simple system structure.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables reliable and automatic creation and updating of neighbor tables, preventing incorrect node positioning and reducing energy waste by ensuring only necessary nodes react to messages from relevant neighbors, thus enhancing energy efficiency and reducing operational costs.
Implementation Method 1
sensing a physical parameter (e.g. light intensity) with a sensor of a first node
Data Source
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AI summary
The present invention provides a method for commissioning of nodes of a network. The method comprises the steps of (S10) receiving, at a first node (30a) of the network, at least one indication message including identification information of a second node (30b) of the network; (S20) receiving, at the first node (30a), parameter information indicating a parameter sensed with at least one parameter sensor associated with the first node (30a); (S30) determining whether the at least one indication message and the parameter information temporarily correlate; and (S40),if a correlation is determined, adding correlation information about the second node (30b) to a register table of the first node (30a).