Consensus-Based Node Retirement in Mesh Networks
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Solution Overview
Problem
Mesh networks face performance degradation due to the presence of weaker nodes, which can slow down the entire network, as the performance is determined by the weakest node, leading to a need for efficient methods to identify and retire or reduce the use of such nodes to maintain optimal performance.
Innovation Solution
A consensus-based node retirement system where nodes monitor each other's performance metrics against a mesh membership contract, allowing the network to self-regulate by identifying and removing nodes that fail to meet performance standards, thereby maintaining network strength through a decentralized and self-governing approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If weaker nodes are allowed to remain in the mesh network, then device diversity and ease of joining are maintained, but overall network performance degrades because the performance is determined by the weakest node
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting node capabilities based on performance metrics. Nodes transition between different capability states (full capabilities, reduced capabilities, or retirement) based on their performance relative to the mesh membership contract, allowing the network to optimize for performance while maintaining a pathway for node participation
Solution Approach 2:
The system implements dynamics through the consensus-based node retirement process where node status is not static but can change over time. Nodes can be added, have capabilities removed, or be retired based on ongoing performance evaluation, creating a dynamic adaptation mechanism that balances performance optimization with node diversity
2Productivity
If a centralized approach is used to manage node retirement, then performance optimization is achieved, but network complexity and single point of failure risks increase
Solution Approach 1:
The mesh network implements self-service through automated performance monitoring and consensus-based decision-making. Nodes automatically report their performance metrics, and the network collectively determines which nodes should have capabilities removed or be retired, eliminating the need for external centralized management while maintaining performance optimization
Solution Approach 2:
The system uses feedback mechanisms where nodes continuously report performance metrics to the network, and this feedback drives the consensus process for capability removal or retirement. The feedback loop ensures that performance optimization decisions are based on actual measured data rather than centralized assumptions
3Productivity
If nodes are immediately retired when performance thresholds are not met, then network performance is optimized, but data loss and functionality disruption occur
Solution Approach 1:
The patent applies preliminary action through the capability removal process that occurs before complete node retirement. By progressively removing capabilities and transitioning nodes through intermediate states, the network prepares for node exit in advance, allowing for data migration and service redistribution before the node fully leaves the network
Solution Approach 2:
The system provides beforehand cushioning by maintaining nodes in a reduced-capability state rather than immediately retiring them. This cushioning period allows the network to redistribute workloads and migrate data before the node is fully retired, preventing sudden performance drops and data loss
Data Source
AI summary
Systems and methods are disclosed for a consensus-based node retirement in a mesh network. An example system includes: a mesh network comprising a plurality of nodes, including a first node, a second node, and a mesh controller; a processor; and memory. The memory may store instructions that, when executed by the one or more processors, may cause the mesh controller to: distribute a mesh membership contract to the mesh network, the mesh membership contract comprising membership rules; receive, from the first node, based on a performance status of the second node, an identification of a deficiency in a performance metric of the second node, wherein the deficiency is based on a failure of the second node to satisfy a membership rule; and remove, after a consensus by the mesh network for the identified deficiency, one or more capabilities of the second node from the mesh network.