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Problem <p>Significant problems for Wireless Sensor Networks (WSNs) include scalability, energy efficiency, and best network performance. Traditional routing and clustering methods frequently fail to strike a balance between energy efficiency and computational cost, resulting in early node failures, longer communication latency, and a shorter network lifespan.</p> Method <p>The Improved Monkey Search Routing with Hybrid PSO-based Fuzzy Multi-Criteria Clustering (EMRHPFC) model is put forth as a solution to these challenges. For optimum cluster head (CH) selection and computational efficiency, this model combines Particle Swarm Optimization (PSO). It makes use of a multi-criteria clustering strategy based on node energy, node degree, nodal distance, and residual energy. The model employs a first-order radio model to precisely measure energy usage during data transmission and reception. To minimize overhead, a distributed re-clustering approach is used to retain cluster heads across several rounds. The Fuzzy C-Mean (FCM) clustering approach ensures that energy is distributed fairly across nodes and that clusters are formed effectively, while the Enhanced Monkey Search Algorithm (E-MSA) is used to improve route pathways by taking into account CH position and energy.</p> Result <p>According to simulation data, the EMRHPFC model considerably improves the performance of WSNs. It achieves a communication latency of 115.46&#xa0;ms, an energy efficiency of 91.19%, a data success rate of 91.28%, a network throughput of 768.17 Kbps, and a routing overhead of 923 packets.</p> Conclusion <p>The major issues, like energy consumption, routing efficiency, and scalability, the EMRHPFC model provides a strong solution for improving the performance of WSNs. The model integrates PSO, fuzzy multi-criteria clustering, and E-MSA to ensure optimum cluster formation and energy-aware routing. The first-order radio model enables accurate energy monitoring, which contributes to a longer network lifespan and lower communication costs.</p>

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Enhanced Monkey Search Routing with Hybrid PSO-Based Fuzzy Multi-criteria Clustering Model in Wireless Sensor Network

  • 期刊论文

  • 发表日期：

    2025年10月10日

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  • Dhananjay Arun Kumbhar,
  • R. R. Dube

摘要

Problem

Significant problems for Wireless Sensor Networks (WSNs) include scalability, energy efficiency, and best network performance. Traditional routing and clustering methods frequently fail to strike a balance between energy efficiency and computational cost, resulting in early node failures, longer communication latency, and a shorter network lifespan.

Method

The Improved Monkey Search Routing with Hybrid PSO-based Fuzzy Multi-Criteria Clustering (EMRHPFC) model is put forth as a solution to these challenges. For optimum cluster head (CH) selection and computational efficiency, this model combines Particle Swarm Optimization (PSO). It makes use of a multi-criteria clustering strategy based on node energy, node degree, nodal distance, and residual energy. The model employs a first-order radio model to precisely measure energy usage during data transmission and reception. To minimize overhead, a distributed re-clustering approach is used to retain cluster heads across several rounds. The Fuzzy C-Mean (FCM) clustering approach ensures that energy is distributed fairly across nodes and that clusters are formed effectively, while the Enhanced Monkey Search Algorithm (E-MSA) is used to improve route pathways by taking into account CH position and energy.

Result

According to simulation data, the EMRHPFC model considerably improves the performance of WSNs. It achieves a communication latency of 115.46 ms, an energy efficiency of 91.19%, a data success rate of 91.28%, a network throughput of 768.17 Kbps, and a routing overhead of 923 packets.

Conclusion

The major issues, like energy consumption, routing efficiency, and scalability, the EMRHPFC model provides a strong solution for improving the performance of WSNs. The model integrates PSO, fuzzy multi-criteria clustering, and E-MSA to ensure optimum cluster formation and energy-aware routing. The first-order radio model enables accurate energy monitoring, which contributes to a longer network lifespan and lower communication costs.