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<p>Industrial Wireless Sensor Network (IWSN) is a system that is becoming more and more popular for use in resource-constrained environments with limited energy, computation, memory, and storage. Two difficult IWSN challenges are maximizing the coverage region and extending the network lifespan. Detecting and relocating redundant nodes is an effective method for increasing the coverage region and longevity of networks. However, most of the energy-efficient protocols are designed, but accurate detection is more challenging. To overcome these challenges, Redundancy based Energy Efficient Routing Protocol (REERP) is employed. The sensor nodes in the industrial wireless sensor network are initially placed at random. The next stage is to find out whether the nearby nodes are identifying comparable data by using a Similarity Graph Neural Network (SGNN). This indicates that the node is redundant if similar information is found. Relocating nodes with a discovered problematic node using a Deep Neural Network (DNN) based on certain characteristics is done after finding the redundant nodes. The best weights in the DNN classifier are then chosen using the Remora Optimisation Algorithm (ROA). In an industrial wireless sensor network, replacing a malfunctioning node with a redundant node can improve each node’s energy, availability, and network lifetime. The simulation analysis shows that the REERP approach has a network lifetime of 986&#xa0;s and 14.4&#xa0;J residual energy, whereas the developed SGNN strategy takes 511s to locate redundant nodes. Therefore, the REERP method is the preferable option for moving the redundant node in order to address the issues of energy consumption and network lifetime in IWSN.</p>

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Relocating redundant node based on similarity Graph Neural Network and Deep Neural Network with Remora Optimization in IWSN

  • 期刊论文

  • 发表日期：

    2026年3月14日

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  • D. Prabakar,
  • S. Prabu,
  • Mukesh Madanan,
  • Shrikant Tiwari

摘要

Industrial Wireless Sensor Network (IWSN) is a system that is becoming more and more popular for use in resource-constrained environments with limited energy, computation, memory, and storage. Two difficult IWSN challenges are maximizing the coverage region and extending the network lifespan. Detecting and relocating redundant nodes is an effective method for increasing the coverage region and longevity of networks. However, most of the energy-efficient protocols are designed, but accurate detection is more challenging. To overcome these challenges, Redundancy based Energy Efficient Routing Protocol (REERP) is employed. The sensor nodes in the industrial wireless sensor network are initially placed at random. The next stage is to find out whether the nearby nodes are identifying comparable data by using a Similarity Graph Neural Network (SGNN). This indicates that the node is redundant if similar information is found. Relocating nodes with a discovered problematic node using a Deep Neural Network (DNN) based on certain characteristics is done after finding the redundant nodes. The best weights in the DNN classifier are then chosen using the Remora Optimisation Algorithm (ROA). In an industrial wireless sensor network, replacing a malfunctioning node with a redundant node can improve each node’s energy, availability, and network lifetime. The simulation analysis shows that the REERP approach has a network lifetime of 986 s and 14.4 J residual energy, whereas the developed SGNN strategy takes 511s to locate redundant nodes. Therefore, the REERP method is the preferable option for moving the redundant node in order to address the issues of energy consumption and network lifetime in IWSN.