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Wireless Sensor Networks (WSNs) play a crucial role ranging from environmental control to queue tracking. These networks are established by deploying numerous tiny sensor nodes that communicate with each other wirelessly. However, the routing paths in WSNs often suffer from delays in timely information delivery. The proposed Multi Objective Grey Wolf Optimization (MOGWO) aims to address these challenges where information transmission may converge slowly and routing paths may struggle in large-dimensional spaces. The objective of MOGWO is to efficiently send information by selecting the shortest path based on the regions. The MOGWO focuses on achieving high efficiency by choosing the shortest path to explore information. In the context of network routing MOGWO optimizes the path to the destination considering the energy levels of the sensor nodes. The proposed MOGWO method demonstrates superior performance compared to existing methods such as PSO, Black Widow Optimization (BWO) and Ant Colony Optimization (ACO). Specifically, the MOGWO method yields better results, achieving an energy consumption of 43 J, Packet Delivery Ratio (PDR) of 90%, and End-To-End Delay (ETED) of 33 s. This outperforms existing methods, highlighting the effectiveness of the MOGWO approach in enhancing network routing paths in WSN.

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An Optimal Cluster Head and Secure Route Path Selection Using Multi Objective—GWO Approach in Wireless Sensor Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Vijaya Bhaskar Reddy Muvva,
  • Zainab abed Almoussawi,
  • Narayan Naik,
  • Sowmya Madhavan,
  • Hirald Dwaraka Praveena

摘要

Wireless Sensor Networks (WSNs) play a crucial role ranging from environmental control to queue tracking. These networks are established by deploying numerous tiny sensor nodes that communicate with each other wirelessly. However, the routing paths in WSNs often suffer from delays in timely information delivery. The proposed Multi Objective Grey Wolf Optimization (MOGWO) aims to address these challenges where information transmission may converge slowly and routing paths may struggle in large-dimensional spaces. The objective of MOGWO is to efficiently send information by selecting the shortest path based on the regions. The MOGWO focuses on achieving high efficiency by choosing the shortest path to explore information. In the context of network routing MOGWO optimizes the path to the destination considering the energy levels of the sensor nodes. The proposed MOGWO method demonstrates superior performance compared to existing methods such as PSO, Black Widow Optimization (BWO) and Ant Colony Optimization (ACO). Specifically, the MOGWO method yields better results, achieving an energy consumption of 43 J, Packet Delivery Ratio (PDR) of 90%, and End-To-End Delay (ETED) of 33 s. This outperforms existing methods, highlighting the effectiveness of the MOGWO approach in enhancing network routing paths in WSN.