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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Vydáno v:Ad hoc networks Ročník 177; s. 103906
Hlavní autoři: Duan, Ying, Fu, Tongyao, Li, Lingling, Pace, Pasquale, Aloi, Gianluca, Fortino, Giancarlo
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.10.2025
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ISSN:1570-8705
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Abstract 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.
AbstractList 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.
ArticleNumber 103906
Author Duan, Ying
Fortino, Giancarlo
Pace, Pasquale
Fu, Tongyao
Aloi, Gianluca
Li, Lingling
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  givenname: Tongyao
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  surname: Fu
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  givenname: Lingling
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  givenname: Pasquale
  surname: Pace
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  givenname: Giancarlo
  surname: Fortino
  fullname: Fortino, Giancarlo
  email: g.fortino@unical.it
  organization: Department of Informatics, Modelling, Electronics and Systems (DIMES), University of Calabria, Via P. Bucci, Rende, CS 87036, Italy
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Keywords Automated guided vehicle-integrated noise-aware adaptive clustering algorithm
Industrial wireless noise model
Industrial wireless sensor network
Language English
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Snippet Industrial Wireless Sensor Networks (IWSNs) play a critical role in real-time monitoring and data collection in smart factories. However, energy constraints in...
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SubjectTerms Automated guided vehicle-integrated noise-aware adaptive clustering algorithm
Industrial wireless noise model
Industrial wireless sensor network
Title AGV-Integrated Noise-Aware Adaptive Clustering for Industrial Wireless Sensor Networks in smart factories
URI https://dx.doi.org/10.1016/j.adhoc.2025.103906
Volume 177
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