EMO-PEGASIS: WSNにおけるエネルギー遅延最適化のためのデュアルフェーズ機械学習プロトコル
1Institute of Applied Computer Science, Lodz University of Technology, ul. Stefanowskiego 18, 90-537 Lodz, Poland.
Sensors (Basel, Switzerland)
|January 28, 2026
まとめ
強化マルチオブジェクティブPEGASIS (EMO-PEGASIS) は、機械学習を使用してエネルギー効率を高め、遅延を削減することにより、ワイヤレスセンサーネットワークを改善します。このプロトコルは、ネットワーク寿命と安定性を大幅に向上させます。
科学分野:
- コンピュータサイエンス; 電気工学; ネットワーク工学
背景:
- ワイヤレスセンサーネットワーク (WSN) は、エネルギー節約とデータ伝送遅延の間の重要なトレードオフに直面しています。PEGASISのような既存のプロトコルはエネルギー効率を提供しますが、高いレイテンシと不均衡な負荷分散に悩まされています。従来のWSNプロトコルにおける最適ではないクラスター形成は、全体的なネットワークパフォーマンスを制限します。
主な方法:
- プロトコル設計と実装のために、デュアルフェーズ機械学習戦略が採用されました。K平均法クラスタリングは、ネットワークの堅牢な空間分割に使用されました。K近傍法 (K-NN) 分類は、適応的でインテリジェントなルーティングに使用されました。
結論:
- EMO-PEGASISプロトコルは、WSNにおけるマルチオブジェクティブ最適化問題に効果的に対処します。機械学習技術の統合は、WSNパフォーマンスを大幅に向上させます。EMO-PEGASISは、エネルギーおよび遅延制約のあるWSN環境に対して信頼性の高いマルチオブジェクティブ最適化を提供します。
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