Takagi-Sugeno fuzzy logic scales Substrate blockchain nodes

Researchers have developed a Takagi-Sugeno fuzzy inference system to dynamically scale validator nodes in private Substrate blockchains. This system analyzes live blockchain parameters like block production time and active node count to recommend scaling actions, aiming to optimize resource usage and performance. The controller uses a 27-rule base and recalibrated membership functions, demonstrating stable autonomous adjustments in closed-loop experiments on a 10-node network storing Queensland Government data. AI

IMPACT This research could lead to more efficient and stable private blockchain networks by enabling autonomous resource management.

RANK_REASON Academic paper detailing a novel fuzzy inference system for blockchain node scaling. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.AI →

• Queensland Government
• substrate
• Takagi-Sugeno fuzzy-model-based fault detection for networked control systems with Markov delays

• paper
• infra

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