産業・制御ネットワークにおける耐障害性無線通信のためのスケーラブルなプロセス認識型ルーティングアーキテクチャ
A scalable process aware routing architecture for resilient wireless communication in industrial and control networks (原題)
A. Alghawli, Ali Raza, Altahir Saah Ahmed, Suzan Hassan Bakhit, M. Farman
🤖 gxceed AI 要約
日本語
産業用炭素回収プラントの分散センサー・制御器間通信の信頼性向上を目的に、スタークラスタと冗長メッシュを組み合わせたプロセス認識型WSN「MeshStar」を提案。表形式Q学習で復旧・トラフィック制御を最適化し、OMNeT++/INETとSimulinkの連成シミュレーションで評価した。95パーセンタイル遅延84.6ms、PDR98.32%、経路復旧3.74秒を達成し、最強ベースラインを上回った。ただしソフトウェア検証中心で実機展開には更なる検証が必要。
English
This study proposes MeshStar, a process-aware wireless sensor network combining star clusters with redundant mesh links for industrial carbon-capture plants. A tabular Q-learning controller optimizes recovery and traffic management using reliability, latency, congestion, energy, and process-criticality signals. Evaluated via OMNeT++/INET and Simulink co-simulation across 30 seeds, it achieved 84.6 ms 95th-percentile latency, 98.32% PDR, and 3.74 s recovery, outperforming baselines. Validation is software-dominant, indicating comparative feasibility rather than field readiness.
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
CCUSは日本のGX実現の鍵となる技術だが、本論文は通信インフラの技術的側面に焦点を当てており、SSBJや有報などの開示制度との直接的な接点は薄い。ただし、炭素回収プラントの安定運用はScope1排出削減の実効性を左右するため、産業部門の脱炭素実装に関心のある読者には参考になる。
In the global GX context
While CCUS is central to global net-zero pathways, this paper addresses the communication infrastructure layer rather than disclosure frameworks like TCFD or ISSB. It contributes to the operational reliability of carbon-capture plants, which underpins the credibility of industrial decarbonization claims, but offers limited direct insight for disclosure scholars.
👥 読者別の含意
🔬研究者:産業用無線ネットワークとプロセス制御の統合に関心のある研究者にとって、Q学習を用いた耐障害性ルーティングの実証的比較を提供する。
🏢実務担当者:CCUSプラントの通信システム設計者にとって、冗長性とプロセス認識型制御を組み合わせたアーキテクチャの実装可能性を示す。
📄 Abstract（原文）
Industrial carbon-capture plants depend on timely and reliable communication among distributed sensors, controllers, and process models. Existing studies commonly examine fault recovery, energy-aware routing, reinforcement learning, or digital-twin monitoring as separate problems, leaving limited evidence on their coordinated use in one reproducible industrial communication framework. This study presents a process-aware MeshStar wireless sensor network (WSN) that combines low-overhead star clusters with redundant mesh links among cluster routers. A tabular Q-learning controller selects recovery and traffic-management actions from network reliability, latency, congestion, residual energy, link availability, and thermal-process criticality. The framework is evaluated through OMNeT++/INET network simulation coupled with a MATLAB/Simulink carbon-capture process model. Thirty independent seeds are used for each method and operating scenario, with comparisons against static Dijkstra, RPL-MRHOF, energy-aware RPL, conventional Q-routing, and deep Q-network routing. Under the revised evaluation protocol, the proposed method achieved a 95th-percentile latency of 84.6 ms, a packet delivery ratio (PDR) of 98.32%, and a route-recovery time of 3.74 s, corresponding to improvements of 12.6%, 0.78 percentage points, and 16.1%, respectively, over the strongest baseline. Scalability, security stress, process stability, and scenario-based economic results are reported separately. Because the validation is software-dominant, the findings demonstrate comparative feasibility rather than field-deployment readiness.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://www.nature.com/articles/s41598-026-72016-w_reference.pdffirst seen 2026-10-16 05:37:49 · last seen 2026-11-06 05:30:35
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