Centralized Data Rate Optimization in Mesh Networks
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Solution Overview
Problem
Existing data rate selection methods in Low Power and Lossy Networks (LLNs) take a 'one-size-fits-all' approach, failing to adapt to varying network conditions and affecting both network performance and topology, as they balance communication range, reliability, throughput, and latency.
Innovation Solution
A centralized device collects network topology, traffic, and performance data to simulate data rate changes, sending commands to nodes to optimize data rate allocations based on simulated results, considering Service Level Agreements (SLAs) and potential impacts on network metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a lower data rate is selected, then communication range and message reliability are improved, but throughput and latency performance deteriorate
Solution Approach 1:
The patent applies local quality by allowing different nodes in the mesh network to use different data rates based on their specific network conditions, traffic requirements, and positions. Instead of a uniform data rate across the entire network, each node can independently select its optimal data rate, enabling local optimization of reliability versus throughput tradeoffs.
Solution Approach 2:
The patent implements dynamics by enabling data rates to be adjusted dynamically based on changing network conditions, traffic patterns, and performance requirements. Nodes can change their data rates over time rather than being fixed, allowing the network to adapt to varying conditions and optimize the balance between reliability and throughput as needs change.
2Productivity
If a higher data rate is selected, then throughput and latency performance are improved, but communication range and message reliability deteriorate
Solution Approach 1:
The patent allows different regions of the network to have different data rate characteristics. Nodes with high throughput requirements can operate at higher data rates while nodes requiring high reliability can use lower data rates, creating local quality variations that optimize overall network performance.
Solution Approach 2:
The system enables dynamic adjustment of data rates based on real-time network conditions. When throughput requirements increase, nodes can raise their data rates; when reliability becomes critical, nodes can lower their data rates, creating a dynamic response to changing operational requirements.
3Device complexity
If a single data rate is configured for all neighboring devices, then device complexity is reduced, but network performance optimization and adaptability to varying conditions are limited
Solution Approach 1:
The patent segments the network into independent nodes, each capable of autonomously selecting its own data rate. This segmentation allows the network to achieve high adaptability through distributed decision-making while keeping individual node complexity low, as each node only needs to make local decisions rather than manage global network state.
Solution Approach 2:
Each node in the mesh network performs self-service by independently selecting its own data rate based on its specific requirements and local network conditions. This eliminates the need for complex centralized control while maintaining high network adaptability, as each node serves itself optimally without increasing overall system complexity.
Data Source
AI summary
In one embodiment, a method is disclosed in which a device receives data regarding a topology of a network. Traffic data for one or more data links in the network and performance data for the one or more data links are also received. A data rate change is simulated for the one or more data links using the topology data, traffic data, and performance data. Based on the simulated data rate change, a data rate change command is provided to one or more nodes associated with the one or more data links.