Scholarlink

<p>In mobile ad hoc networks (MANETs) secured data transmission is crucial for improving the network reliability and efficiency. This study aims to predict a secure way to transmit a data packet to a destination node by developing the route building (RB) stage to ensure secure transmission. The proposed methodology consists of two phases; initially, we harness the Salp Swarm Optimization approach to perform routing refinements and the Cat Swarm Optimization method employed for conducting trust calculations. To improve the network adaptability, we utilized a graphical neural network (GNNs) to develop Virtual Domain Construction (VRC), which creates virtual groups determined by the node power, transmission mode, transmission type packet, as well as distance between the nodes. We build temporary clusters to get around common clustering problems while still satisfying the scalability requirements. The proposed system was simulated using an NS2 simulator and the effectiveness of the system was analysed using the parameters including throughput (96.00%), packet delivery ratio (97.00%), end-to-end delay (3.04s), and network lifetime (95.63%) outperforms the existing methodologies.</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

Intelligent techniques for processing and taxonomy of smart City data in mobile ad hoc networks

  • 期刊论文

  • 发表日期：

    2025年7月1日

查看全文
  • M. Sumithra,
  • M. Vidhyalakshmi,
  • Soma Umamaheshwar,
  • Naveen Mukkapati

摘要

In mobile ad hoc networks (MANETs) secured data transmission is crucial for improving the network reliability and efficiency. This study aims to predict a secure way to transmit a data packet to a destination node by developing the route building (RB) stage to ensure secure transmission. The proposed methodology consists of two phases; initially, we harness the Salp Swarm Optimization approach to perform routing refinements and the Cat Swarm Optimization method employed for conducting trust calculations. To improve the network adaptability, we utilized a graphical neural network (GNNs) to develop Virtual Domain Construction (VRC), which creates virtual groups determined by the node power, transmission mode, transmission type packet, as well as distance between the nodes. We build temporary clusters to get around common clustering problems while still satisfying the scalability requirements. The proposed system was simulated using an NS2 simulator and the effectiveness of the system was analysed using the parameters including throughput (96.00%), packet delivery ratio (97.00%), end-to-end delay (3.04s), and network lifetime (95.63%) outperforms the existing methodologies.