MOGAとLSAの最適化によるクラスターベースのWSNルーティング
1School of Cybersecurity, Jiangxi Police Institute, Jiangxi, China. tanweiqin@jxga.edu.cn.
Scientific reports
|February 19, 2026
まとめ
この研究は,多目的遺伝子アルゴリズム (MOGA) とライトニング検索アルゴリズム (LSA) を組み合わせた新しい最適化方法を導入し,ワイヤレスセンサーネットワーク (WSN) を強化します. このアプローチは,エネルギー消費を大幅に削減し,IoTアプリケーションのネットワーク寿命を延長します.
科学分野:
- コンピュータサイエンス コンピュータサイエンス
- 電気工学 電気工学とは
- ネットワークエンジニアリング ネットワークエンジニアリング
背景:
- ワイヤレスセンサーネットワーク (WSN) は,環境モニタリングと産業自動化に不可欠ですが,エネルギー制限に直面しています.
- 既存のクラスタリングとルーティングの方法は,エネルギー効率,ネットワークの寿命,サービスの品質のバランスをとるのに苦労しています.
研究 の 目的:
- 現在のエネルギー効率とルーティング技術の限界を克服するWSNのための新しい最適化方法を開発する.
- ネットワークの寿命,データ配信速度を向上させ,大規模WSNにおけるエネルギー消費を削減する.
主な方法:
- 多目的遺伝子アルゴリズム (MOGA) とライトニング検索アルゴリズム (LSA) を組み合わせたハイブリッド最適化アプローチです.
- MOGAは,クラスター内の距離とエネルギー消費を最小限に抑え,最適なクラスターヘッド (CHs) を選択するために使用されます.
- LSAは,データ伝送のための世界的に最適な経路を効率的に特定するために統合されています.
主要な成果:
- 提案されたMOGA-LSA方法は,MOGAGSA,LEACH,PSOと比較して,エネルギー消費を48%削減しました.
- データ配信速度の改善とネットワーク寿命の延長が実証されています.
- シミュレーション結果は,エネルギー消費,パケット損失,エンドツーエンドの遅延において効率的なパフォーマンスを示しています.
結論:
- メタヒューリスティックアルゴリズムの組み合わせは,単一のアルゴリズムの限界を解決し,大規模WSNのための包括的なソリューションを提供します.
- 提案された方法は,モノのインターネット (IoT) とスマート環境アプリケーションの持続性と信頼性を高めます.
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