Closed-Loop Dynamic Validator Node Scaling in Private Substrate Blockchains Using Takagi-Sugeno Fuzzy Inference

Jul 16, 2026

Private blockchain networks with fixed validator configurations cannot adapt to changing workload. Inefficient sizing wastes resources when load is light and degrades performance when demand rises, making validator count selection a time-varying, multi-factor problem. This paper proposes a Takagi-Sugeno fuzzy inference controller that ingests live block production time, block size, and active validator count to output an efficiency score and a Scale Up/Maintain/Scale Down action. Empirical recalibration of membership functions anchors rules to observed testbed ranges. Experiments on a 10-node Substrate network using real smart water meter data hashes show distinct provisioning profiles for 4/7/10 active nodes, and closed-loop convergence to a stable equilibrium with fewer scaling oscillations than threshold baselines while maintaining comparable block production times.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

Abstract

Introduction

Problem of Fixed Validator Configurations

Fuzzy Logic and Takagi-Sugeno for Control

Related Work and Research Gap

Proposed TS Fuzzy Inference System

Closed-Loop Dynamic Validator Node Scaling in Private Substrate Blockchains Using Takagi-Sugeno Fuzzy Inference Thandile Nododile, Ayinde M. Usman, Clement N. Nyirenda Department of Computer Science University of the Western Cape, South Africa 0009-0000-1386-2425, 0000-0003-1926-3508, 0000-0002-4181-0478 arXiv :2607 .0790 1v 1 [ cs .CR] 8 Jul 2026 Abstract—Private blockchain networks operate with fixed node configurations that cannot adapt to changing workload conditions. When too many nodes serve a light workload, resources are wasted; when too few nodes face heavy demand, block production slows and finalisation degrades. The right number of validator nodes is hard to determine, as it depends on multiple overlapping factors that shift over time. This paper presentsa Takagi-Sugeno (TS) fuzzy inference system that reads live blockchain parameters, namely block production time, block size, and active node count, and outputs a continuous efficiency score alongside a scaling recommendation: Scale Up, Maintain, or Scale Down. The controller uses triangular membership functions across three linguistic variables, evaluated through a complete 27-rule base with product t-norm aggregation. A central methodological contribution is an empirical recalibration of the membership functions, anchoring the linguistic terms to the observed operating range of the testbed rather than to theoretical extremes. The system is evaluated on a 10-node Substrate blockchain network storing real smart water meter data hashes from the Queensland Government open data portal. Statistical analysis across validator configurations of 4, 7, and 10 active nodes confirms that the controller produces distinct operational profiles that correctly reflect each configuration’s provisioning state. In closed-loop experiments, the controller autonomously adjusts validator participation in both directions, activating validators under rising load and removing them under over-provisioning, and converges to the same stable equilibrium from both directions. Compared against three threshold-based baselines, it exhibits substantially fewer scaling oscillations while maintaining comparable block production times. The results demonstrate that TS fuzzy inference can support autonomous validator management in private blockchain deployments, with stable scaling behaviour that threshold approaches cannot match. Index Terms—Adaptive control, blockchain, fuzzy logic, IoT, Substrate, Takagi-Sugeno, validator node scaling I. INTRODUCTION Blockchain technology provides a distributed, tamperresistant ledger architecture suitable for applications requiring data integrity and auditability [1] . Devices in Internet of Things (IoT) environments generate large volumes of data that often lack inherent security. Blockchain offers secure, tamper-resistant storage for such sensor data across domains including healthcare, supply chain, and utility monitoring [2],[3] . Private blockchain networks, where validator participation is restricted to a known set of nodes, are particularly suited to enterprise IoT deployments that require auditability without the overhead of permissionless consensus [4] . Deploying these networks for IoT data management, however, introduces a tension: validator configurations are typically fixed, while IoT workloads vary with environmental conditions, time-of-day patterns, and device availability [5] . A configuration sized for peak load wastes computational resources during quiet periods, while one sized for average load cannot maintain acceptable block production times during demand spikes [6] . The challenge of adaptive node scaling is that the decision boundaries are not crisp. Network conditions exist on a continuum, and the appropriate scaling response depends on multiple interacting factors. When block production time is rising while block size is moderate and a subset of the available validatorsis already active, simple threshold rules cannot det

FAQs

Why is dynamic scaling of validator nodes needed in private Substrate blockchains?

Fixed validator configurations cannot match time-varying IoT workloads. When too few validators serve heavy demand, block production slows and finalization degrades; when too many serve light demand, resources are wasted.

What inputs and outputs does the proposed Takagi-Sugeno fuzzy controller use?

The controller reads live block production time, block size, and active node count, then outputs a continuous efficiency score and a discrete scaling recommendation: Scale Up, Maintain, or Scale Down.

How does the paper evaluate the controller’s performance compared with threshold-based baselines?

It runs experiments on a 10-node Substrate testbed using real smart water meter data hashes and analyzes configurations with 4, 7, and 10 active validators. The results compare scaling oscillations and block production time against three threshold-based controllers, showing fewer oscillations with similar block production performance.

Jul 16, 2026

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