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<p>A mobile ad hoc network (MANET) is a group of wireless mobile nodes that create a network without the assistance of a standard support provider or central administrator. Packet transmission in a MANET is enabled by collecting intermediary equipment between the sender and the recipient. As a result of efficient packet transmission, quality of service is improved, including faster throughput, reduced latency, and assured delivery. However, challenges such as frequent topology changes and unpredictable traffic patterns caused by dynamic node mobility and uneven data distribution can significantly degrade network performance. To overcome these problems, an Enhanced QoS-aware Routing approach using an Optimized Deep Learning framework (ERODE) has been proposed for effective routing and data transmission in MANETs. The ERODE approach integrates Adaptive Neuro-Fuzzy Inference System (ANFIS) for retransmission control and Tyrannosaurus Optimization Algorithm (TOA) for energy-efficient routing. By optimizing parameters including throughput, end-to-end (E2E) delay, Network Lifetime (NL), and packet delivery ratio (PDR), the ERODE method improves network performance and reliability under dynamic conditions. The proposed method achieves an EC of 31.56&#xa0;J, while the existing AFB-GPSR, FLSTMTLAR, and OFC-TR systems attain 35&#xa0;J, 40&#xa0;J, and 45&#xa0;J, respectively. In terms of PDR, the ERODE technique outperforms AFB-GPSR, FLSTMTLAR and OFC-TR by 5.01%, 5.43%, and 7.68%, respectively.</p>

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Enhanced QoS aware routing via optimized deep learning framework in MANET

  • 期刊论文

  • 发表日期：

    2026年1月14日

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  • B. Siva Sankari,
  • A. Ahilan,
  • Sai baba Veldi,
  • S. Rajakumar,
  • Subraja Rajaretnam

摘要

A mobile ad hoc network (MANET) is a group of wireless mobile nodes that create a network without the assistance of a standard support provider or central administrator. Packet transmission in a MANET is enabled by collecting intermediary equipment between the sender and the recipient. As a result of efficient packet transmission, quality of service is improved, including faster throughput, reduced latency, and assured delivery. However, challenges such as frequent topology changes and unpredictable traffic patterns caused by dynamic node mobility and uneven data distribution can significantly degrade network performance. To overcome these problems, an Enhanced QoS-aware Routing approach using an Optimized Deep Learning framework (ERODE) has been proposed for effective routing and data transmission in MANETs. The ERODE approach integrates Adaptive Neuro-Fuzzy Inference System (ANFIS) for retransmission control and Tyrannosaurus Optimization Algorithm (TOA) for energy-efficient routing. By optimizing parameters including throughput, end-to-end (E2E) delay, Network Lifetime (NL), and packet delivery ratio (PDR), the ERODE method improves network performance and reliability under dynamic conditions. The proposed method achieves an EC of 31.56 J, while the existing AFB-GPSR, FLSTMTLAR, and OFC-TR systems attain 35 J, 40 J, and 45 J, respectively. In terms of PDR, the ERODE technique outperforms AFB-GPSR, FLSTMTLAR and OFC-TR by 5.01%, 5.43%, and 7.68%, respectively.