AGV-Integrated Noise-Aware Adaptive Clustering for Industrial Wireless Sensor Networks in smart factories

Industrial Wireless Sensor Networks (IWSNs) play a critical role in real-time monitoring and data collection in smart factories. However, energy constraints in sensor nodes significantly limit the network lifespan. In addition, traditional simulation methods overlook the impact of industrial noise,...

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Veröffentlicht in:Ad hoc networks Jg. 177; S. 103906
Hauptverfasser: Duan, Ying, Fu, Tongyao, Li, Lingling, Pace, Pasquale, Aloi, Gianluca, Fortino, Giancarlo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.10.2025
Schlagworte:
Automated guided vehicle-integrated noise-aware adaptive clustering algorithm
Industrial wireless noise model
Industrial wireless sensor network
Automated guided vehicle-integrated noise-aware adaptive clustering algorithm
Industrial wireless noise model
Industrial wireless sensor network
ISSN:1570-8705
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Zusammenfassung:Industrial Wireless Sensor Networks (IWSNs) play a critical role in real-time monitoring and data collection in smart factories. However, energy constraints in sensor nodes significantly limit the network lifespan. In addition, traditional simulation methods overlook the impact of industrial noise, reducing the truthfulness of experimental results. To address these challenges, we propose an Automated Guided Vehicle-Integrated Noise-Aware Adaptive Clustering (A-INAC) algorithm. The algorithm incorporates an Industrial Wireless Noise Model (IWNM) to reflect noise characteristics in the factory environment and optimizes the selection of cluster directors to achieve more balanced energy consumption. In addition, a hierarchical transmission strategy leveraging the mobility of AGVs is designed to meet large-scale network transmission needs. Simulation results demonstrate that the A-INAC algorithm can effectively reduce network energy consumption and extend network lifetime by 39% and 118% compared to LEACH and LEACH-C, respectively.
ISSN:1570-8705
DOI:10.1016/j.adhoc.2025.103906