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<p>The growing use of wireless sensor network (WSN) has resulted in the growth of demand for efficient, reliable, and secure communication infrastructures. Nonetheless, limitations, including limited energy resources, dynamic topology, and vulnerability to malicious attacks, tend to reduce network performance and reliability. Hence, the proposed research work presents a new Greylag Goose Random Forest-Based Flooding Protocol (GGRFFP) to enhance communication performance in WSNs. The developed hybrid model incorporates Greylag Goose Optimization (GGO) for energy-aware and adaptive cluster head (CH) selection, Random Forest (RF) for identifying malicious nodes, and an enhanced Flooding Protocol (FP) for secure and efficient data transmission. The model was implemented in the NS3 simulation platform, and the experimental results verify that GGRFFP significantly increases energy efficiency, network lifetime (NLT), packet delivery ratio (PDR), throughput, and security accuracy compared to classical routing protocols. The system achieved the maximum detection accuracy (DA), the lowest false alarm rate (FAR), and reductions in energy consumption (EC) and routing overhead (ROH). These results demonstrate that the protocol efficiently reduces redundant transmissions, improves secure communication, and prolongs the operational longevity of wireless sensor network. In the end, GGRFFP offers a scalable, innovative routing paradigm for future industrial and smart-environment applications.</p>

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An intelligent energy-efficient secure flooding in wireless sensor network

  • 期刊论文

  • 发表日期：

    2026年1月18日

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  • S. Mary Evanchalin,
  • R. Ravi

摘要

The growing use of wireless sensor network (WSN) has resulted in the growth of demand for efficient, reliable, and secure communication infrastructures. Nonetheless, limitations, including limited energy resources, dynamic topology, and vulnerability to malicious attacks, tend to reduce network performance and reliability. Hence, the proposed research work presents a new Greylag Goose Random Forest-Based Flooding Protocol (GGRFFP) to enhance communication performance in WSNs. The developed hybrid model incorporates Greylag Goose Optimization (GGO) for energy-aware and adaptive cluster head (CH) selection, Random Forest (RF) for identifying malicious nodes, and an enhanced Flooding Protocol (FP) for secure and efficient data transmission. The model was implemented in the NS3 simulation platform, and the experimental results verify that GGRFFP significantly increases energy efficiency, network lifetime (NLT), packet delivery ratio (PDR), throughput, and security accuracy compared to classical routing protocols. The system achieved the maximum detection accuracy (DA), the lowest false alarm rate (FAR), and reductions in energy consumption (EC) and routing overhead (ROH). These results demonstrate that the protocol efficiently reduces redundant transmissions, improves secure communication, and prolongs the operational longevity of wireless sensor network. In the end, GGRFFP offers a scalable, innovative routing paradigm for future industrial and smart-environment applications.