Network Coordinator for Low Power Sensor Topology Optimization
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Solution Overview
Problem
Low power sensor networks face challenges in optimizing node associations and data transmission in dense environments, leading to inefficient battery usage and data redundancy, especially in applications like shipping logistics where long-term monitoring is required.
Innovation Solution
A network coordinator system that creates optimized tree or star topologies by analyzing residual battery capacity, signal strength, and data redundancy, using machine learning to improve node associations and time-division multiplexing for efficient data collection, ensuring minimal battery usage and optimal data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional flat network associations are used in dense sensor networks, then node connectivity is maintained, but battery consumption increases and data redundancy occurs
Solution Approach 1:
The patent segments the flat network association into hierarchical tree structures with multiple levels. Sensor nodes are organized under parent nodes, creating a segmented communication path that reduces the number of nodes each node needs to communicate with directly, thereby reducing battery consumption while maintaining connectivity through the hierarchical structure.
Solution Approach 2:
The patent introduces a hierarchical dimension to the previously flat two-dimensional network association. By adding the vertical hierarchy level (parent-child relationships), the system transforms the network structure from a flat plane to a multi-level hierarchy, enabling more efficient routing and reduced redundancy without sacrificing connectivity.
2Productivity
If machine learning optimization is applied to node associations, then data transmission efficiency improves, but system complexity increases
Solution Approach 1:
The patent introduces machine learning algorithms as an intermediary layer between the sensor nodes and the network coordinator. This intermediary analyzes network conditions, node positions, and data patterns to automatically optimize association relationships, improving transmission efficiency while isolating the complexity from the individual sensor nodes themselves.
Solution Approach 2:
The system employs self-service mechanisms where the machine learning model automatically adjusts and optimizes node associations based on observed network conditions without requiring manual intervention. The system learns from data patterns and autonomously reconfigures the network topology for optimal performance.
3Use of energy by moving object
If time-division multiplexing is implemented for data collection, then battery usage efficiency improves, but data collection time increases
Solution Approach 1:
The patent implements time-division multiplexing where sensor nodes transmit data in periodic time slots rather than continuously or simultaneously. Each node is assigned specific time windows for transmission, reducing battery usage by keeping radios dormant most of the time while organizing data collection into efficient periodic cycles that minimize total collection time.
Data Source
AI summary
An embodiment of network coordinator apparatus may include a node provisioner to provision each of a plurality of low power nodes, a node associater to create a first association for each of the plurality of lower power nodes, and a node coordinator communicatively coupled to the node provisioner and the node associater to coordinate the plurality of lower power nodes.