Dynamic Lighting Network Brain for Automatic Node Management
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Solution Overview
Problem
Traditional mesh networks require manual configuration and reconfiguration whenever nodes are added or removed, making them inefficient in adapting to an arbitrary or changing number of nodes, particularly in dynamic lighting systems.
Innovation Solution
A self-healing wireless network system with a network brain, base nodes, and basic nodes that communicate using unique identifiers, allowing automatic detection and adaptation of node additions or removals without reconfiguring the network, utilizing broadcast messages and relay mechanisms to maintain a dynamic node list and sensor data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional mesh networks use coordinator nodes to coordinate data, then data coordination is achieved, but manual configuration and reconfiguration are required when nodes are added or removed
Solution Approach 1:
The network enables automatic node detection and registration through broadcast messages. When a node joins the network, it automatically broadcasts its presence and receives acknowledgment from the network brain, eliminating the need for manual configuration. The system self-updates its node list automatically through this broadcast-response mechanism.
Solution Approach 2:
The network implements a feedback mechanism where nodes broadcast their status and the network brain responds with acknowledgment messages. This continuous feedback loop allows the network to automatically detect node additions and removals, maintaining an updated node list without manual intervention.
2Reliability
If coordinator nodes are used in traditional mesh networks, then data coordination is possible, but the network requires reconfiguration when topology changes
Solution Approach 1:
The network transitions from a static coordinator-based model to a dynamic peer-to-peer model where any node can broadcast messages. The network brain dynamically updates the node list based on real-time broadcast messages, allowing the network topology to change without reconfiguration while maintaining stability through the persistent network brain.
3Productivity
If manual configuration is used for each node addition or removal, then network control is precise, but operational efficiency decreases
Solution Approach 1:
The network brain pre-establishes the node list data structure and readiness to receive broadcast messages. When nodes join, they automatically broadcast their presence immediately, and the network brain automatically processes and updates the node list in real-time, eliminating configuration delays.
Data Source
AI summary
A dynamic lighting system may comprise a base node having a broadcast range, a plurality of lights being operatively associated with a set of basic nodes, and a network brain configured to communicate with the base node and store a node list with the unique identifiers for the set of basic nodes. The set of basic nodes may include local nodes in the broadcast range and remote nodes beyond the broadcast range. The network brain may be configured to send a broadcast message that is communicated to local nodes within the broadcast range and remote nodes beyond the broadcast range.