RL-ACO: Reinforcement Learning Adaptive Consensus Optimization for Scalable Blockchain-Based Greenhouse Gas Monitoring
Alick Andrew Sakala, Yu Chen
🤖 gxceed AI 要約
日本語
ブロックチェーンのGHGモニタリングにおけるスケーラビリティ問題を、強化学習（DQN）による動的コンセンサス最適化で解決。West Africaの気候連合を想定した400バリデータで、PBFT比10.8倍のスループットを達成。AIを使ってブロックチェーンの性能とGHGアラートのタイムリー性を同時最適化する新手法。
English
Proposes RL-ACO, a reinforcement learning (DQN) framework for adaptive consensus optimization in blockchain-based GHG monitoring. By dynamically tuning cluster size, block interval, and alert priority, it achieves 3,625 TPS at 400 validators—10.8× PBFT improvement—while maintaining security and achieving 96/100 ISO 14064-3 compliance score, validated on EPA, CDP, and OpenGHG datasets.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本のGHG MRVはサプライチェーン排出量算定の信頼性が課題。本手法はブロックチェーン＋AIで大規模コンソーシアムでも高いスループットを実現し、SSBJ対応のScope3データ検証基盤として応用可能性がある。
In the global GX context
The framework addresses a critical bottleneck in scaling blockchain MRV systems for multi-stakeholder climate coalitions, offering a path to efficient, auditable GHG data infrastructure that aligns with ISSB and CSRD assurance requirements.
👥 読者別の含意
🔬研究者:Novel integration of RL into BFT consensus for GHG MRV, with formal security proofs and sensitivity analysis.
🏢実務担当者:Demonstrates that blockchain-based MRV can be scaled to industrial consortium sizes using RL-based parameter tuning.
🏛政策担当者:Highlights feasibility of tamper-proof GHG monitoring at scale, relevant for carbon market integrity.
📄 Abstract（原文）
Byzantine Fault Tolerant (BFT) consensus protocols underpin data-integrity guarantees in permissioned blockchains, yet their O(N 2 ) message complexity renders them impractical for the large multi-stakeholder consortia required by industrial greenhouse-gas (GHG) Monitoring, Reporting, and Verification (MRV) systems. At N = 400 validators representative of a pan-West-African climate coalition classical PBFT throughput collapses from approximately 2,610 TPS to 337 TPS, violating the minimum viability threshold for continuous IoT-driven emissions tracking. This paper presents RL-ACO, an AI-driven consensus framework that embeds a Deep Q-Network (DQN) agent directly into the consensus control loop. The agent observes a ten-dimensional blockchain state vector and selects from 18 discrete parameter-adjustment actions to dynamically tune cluster count k , block interval I , and emission-alert priority weight ω . A composite climate-aware reward function R(s, a) jointly optimizes throughput, P99 latency, Byzantine fault-tolerance margin, and GHG alert timeliness. Minimum Spanning Tree (MST) hierarchical cluster formation reduces message complexity from O(N 2 ) to O(N log N) , while BLS threshold signature aggregation cuts per-round bandwidth by an order of magnitude. Security and liveness are formally proven under partial synchrony for f < N/3 Byzantine nodes. Evaluated on three public environmental datasets EPA GHGRP, CDP Supply Chain, and OpenGHG RL-ACO sustains 3,625 TPS at N = 400, a 10.8 improvement over PBFT and 3.0 over IBFT 2.0. The DQN agent converges in approximately 1,200 training episodes, raises anomaly-detection F1 from 65.3 % to 91.2 %, and achieves an ISO 14064-3 compliance score of 96/100. An 864-configuration se nsitivity analysis confirms that the framework’s throughput advantage over IBFT 2.0 never falls below +127 % irrespective of workload, Byzantine rate, or hyperparameter choice.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.29322/ijsrp.16.05.2026.p17325first seen 2026-07-13 06:33:16
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