Scholarlink

<p> A Wireless Sensor Network (WSN) is a demanding platform, wherein sensor nodes (SNs) monitor and sense physical as well as environmental circumstances and transfer data to the base station (BS) using routing. The agricultural sector also adopts this network for promoting innovations for environment-friendly farming techniques, less management expense and attaining scientific cultivation. In previous studies, SNs suffer from energy problems, complicated routing processes direct-to-data transmission failures and delays in sensor-enabled agricultural areas. To overcome these issues, this research presented the Fractional Dung Namib Optimizer (FDNO) for routing in WSN. Firstly, the WSN simulation is done. Then, a Deep Recurrent Neural Network (DRNN) is deployed for energy prediction. After that, the cluster formation is done based on the Dung Namib Optimizer (DNO), which is the combination of the Dung beetle optimizer (DBO) and Namib beetle optimization (NBO). Then, cluster head (CH) selection is accomplished by considering parameters includes delay, predicted energy, trust, residual energy and distance. Next, the routing is performed by FDNO, which is designed by the hybridization of Fractional Calculus (FC) with DNO. Finally, data is transmitted to BS for further processing. In addition, FDNO obtained minimal distance and delay of about 26.986&#xa0;m and 0.673&#xa0;ms as well as maximal trust and energy of about 89.211 and 0.175&#xa0;J.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Optimized energy-efficient routing in agriculture using wireless sensor networks

  • 期刊论文

  • 发表日期：

    2025年6月18日

查看全文
  • Y. Lavanya,
  • A. Maheswara Rao

摘要

A Wireless Sensor Network (WSN) is a demanding platform, wherein sensor nodes (SNs) monitor and sense physical as well as environmental circumstances and transfer data to the base station (BS) using routing. The agricultural sector also adopts this network for promoting innovations for environment-friendly farming techniques, less management expense and attaining scientific cultivation. In previous studies, SNs suffer from energy problems, complicated routing processes direct-to-data transmission failures and delays in sensor-enabled agricultural areas. To overcome these issues, this research presented the Fractional Dung Namib Optimizer (FDNO) for routing in WSN. Firstly, the WSN simulation is done. Then, a Deep Recurrent Neural Network (DRNN) is deployed for energy prediction. After that, the cluster formation is done based on the Dung Namib Optimizer (DNO), which is the combination of the Dung beetle optimizer (DBO) and Namib beetle optimization (NBO). Then, cluster head (CH) selection is accomplished by considering parameters includes delay, predicted energy, trust, residual energy and distance. Next, the routing is performed by FDNO, which is designed by the hybridization of Fractional Calculus (FC) with DNO. Finally, data is transmitted to BS for further processing. In addition, FDNO obtained minimal distance and delay of about 26.986 m and 0.673 ms as well as maximal trust and energy of about 89.211 and 0.175 J.